GPT-4.5 is real, it’s documented, and it’s one of the most disappointing model releases in OpenAI’s history. Released February 27, 2025 at $75 per million input tokens (2.5 times the cost of GPT-4o), it delivers a 3.2% improvement on MMLU benchmarks while consuming what likely amounts to 10 times the compute. The company itself admitted it’s “expensive and not a substitute for GPT-4.” That’s not marketing spin. That’s an admission of failure.
GPT-4.5 exists, but you probably shouldn’t use it
This guide documents a real model with real benchmarks and real pricing. GPT-4.5 launched with official API access via the gpt-4.5-preview endpoint, published performance data across six major benchmarks, and a price point that makes Claude Opus 4 look reasonable by comparison.
The positioning is bizarre. OpenAI frames GPT-4.5 as an incremental improvement over GPT-4 with enhanced reasoning capabilities, but the numbers tell a different story. On MMLU, it scores 89.6% compared to GPT-4’s 86.4%. On HumanEval coding tasks, it hits 88.6% versus GPT-4’s 85.2%. These are marginal gains that don’t justify the compute investment or the pricing premium.
What makes this release particularly strange is the timing. Claude Opus 4 shipped in March 2026 with 72.5% on SWE-bench Verified and 14-hour autonomous task completion. GPT-4o already offers multimodal capabilities with vision and voice at $5/$15 per million tokens. GPT-4.5 sits awkwardly between them, offering neither the agentic capabilities of Claude nor the multimodal versatility of its own predecessor.
The market didn’t ask for this. Developers wanted longer context windows (GPT-4.5 stays at 128K while Claude offers 1M token preview access). They wanted better coding performance (GPT-4.5’s 36.7% on AIME reasoning tasks gets crushed by o3-mini’s 87.3%). They wanted lower prices (GPT-4.5 costs 15 times more than GPT-4o per token).
Instead, OpenAI shipped a model that excels at exactly one thing: professional task completion where human evaluators prefer its outputs 63.2% of the time over GPT-4o. That’s a real improvement. But it’s a narrow one that doesn’t justify the infrastructure cost for most use cases.
This guide exists because thousands of developers are searching for GPT-4.5 information, trying to understand whether it fits their workflows. The answer for 90% of teams is no. But for the remaining 10% working on high-stakes professional writing, legal document analysis, or enterprise communications where output quality matters more than cost, GPT-4.5 might be worth the premium.
The rest of this guide breaks down exactly what GPT-4.5 does, where it fails, and how to decide whether you’re in that 10%.
Specs at a glance
| Specification | Value |
|---|---|
| Developer | OpenAI |
| Release Date | February 27, 2025 |
| Model Type | Large Language Model (Text) |
| Architecture | Transformer-based |
| Parameter Count | Not disclosed |
| Context Window | 128,000 tokens |
| Modalities | Text only (no vision or audio) |
| Training Cutoff | Not disclosed |
| API Access | gpt-4.5-preview endpoint |
| Pricing (Input) | $75 per million tokens |
| Pricing (Output) | $150 per million tokens |
| Fine-tuning | Available via OpenAI platform |
| Function Calling | Yes (standard OpenAI implementation) |
| Vision Capabilities | No |
| Audio Support | No |
| Safety Layers | OpenAI Moderation API |
| Open Source | No (closed source, API-only) |
| Key Differentiator | Professional task quality (63.2% human preference vs GPT-4o) |
The 128,000 token context window is the same as GPT-4o and GPT-4 Turbo. That’s 96,000 words of input, enough for a full novel or a complex legal brief. But it’s not competitive with Claude Opus 4’s 200,000 token standard window or the 1 million token preview access Anthropic offers for extended research tasks.
The pricing structure is where GPT-4.5 becomes problematic for most developers. At $75 per million input tokens and $150 per million output tokens, a typical 2,000-word conversation with a 500-word response costs about $0.26. That’s 15 times more expensive than the same conversation on GPT-4o, which would cost roughly $0.017. Running 1,000 such conversations per day costs $260 on GPT-4.5 versus $17 on GPT-4o.
The lack of multimodal capabilities is a step backward. GPT-4o ships with vision and voice. GPT-4.5 strips those features out entirely, returning to text-only processing. OpenAI hasn’t explained this regression, but the implication is clear: the architectural changes that deliver GPT-4.5’s quality improvements don’t translate to multimodal inputs.
GPT-4.5 benchmarks show marginal gains at massive cost
| Benchmark | GPT-4.5 | GPT-4o | Claude Opus 4 | o3-mini | GPT-4 |
|---|---|---|---|---|---|
| MMLU (Knowledge) | 89.6% | 88.7% | ~90% (est.) | ~87% | 86.4% |
| HumanEval (Coding) | 88.6% | 90.2% | ~92% (est.) | ~89% | 85.2% |
| GPQA (Science) | 71.4% | 53.6% | No data | ~68% | ~50% |
| SimpleQA (Factual) | 62.5% | No data | No data | No data | ~58% |
| AIME (Math Reasoning) | 36.7% | ~35% | No data | 87.3% | ~33% |
| Human Preference (Pro Tasks) | 63.2% vs GPT-4o | Baseline | No data | No data | ~45% vs GPT-4o |
| Pricing (Input/Output per 1M) | $75 / $150 | $5 / $15 | $15 / $75 | $1.10 / $4.40 | $30 / $60 |
GPT-4.5 wins on exactly two benchmarks: GPQA science questions (71.4% versus GPT-4o’s 53.6%) and human preference ratings on professional tasks (63.2% preference over GPT-4o). The GPQA improvement is substantial, jumping nearly 18 percentage points. That matters for scientific writing, research synthesis, and technical documentation where domain knowledge accuracy is critical.
But look at the losses. GPT-4o beats GPT-4.5 on HumanEval coding tasks by 1.6 percentage points. That’s not a huge gap, but it means GPT-4.5 is worse at code generation than its predecessor despite costing 15 times more. And o3-mini absolutely destroys GPT-4.5 on AIME mathematical reasoning, scoring 87.3% versus 36.7%. That’s a 50-point gap.
The human preference data is the only genuinely impressive number here. When evaluators compared GPT-4.5 and GPT-4o outputs on professional writing tasks (legal briefs, business reports, technical documentation), they preferred GPT-4.5’s work 63.2% of the time. That’s a statistically significant preference that suggests real quality improvements in tone, structure, and argumentation.
Here’s the problem: that 63.2% preference doesn’t translate to measurable productivity gains in most workflows. A legal team might prefer GPT-4.5’s draft contracts, but if GPT-4o’s drafts require 10% more editing time while costing 93% less, the math still favors GPT-4o. The quality improvement has to be dramatic enough to justify the cost differential, and for most use cases, it’s not.
Claude Opus 4 scores 72.5% on SWE-bench Verified, the gold standard for real-world coding tasks. GPT-4.5 has no published SWE-bench score. That absence is telling. OpenAI knows developers care about coding performance, and if GPT-4.5 had competitive numbers, they’d publish them.
Professional task quality is GPT-4.5’s only real advantage
The 63.2% human preference win rate on professional tasks is GPT-4.5’s signature feature. This isn’t about emotional intelligence or conversational warmth. It’s about output quality in high-stakes professional contexts where tone, structure, and argumentation matter as much as factual accuracy.
Technically, this likely comes from additional reinforcement learning focused specifically on professional writing scenarios. OpenAI hasn’t disclosed the training methodology, but the pattern is clear: evaluators consistently prefer GPT-4.5’s outputs when the task involves formal communication, complex argumentation, or domain-specific expertise presentation.
The proof is in the numbers. A 63.2% preference rate means that in roughly two out of every three head-to-head comparisons, human evaluators chose GPT-4.5’s output over GPT-4o’s. That’s not a marginal difference. It’s a clear quality gap.
Use this feature when you’re drafting legal documents, preparing investor presentations, writing technical whitepapers, or generating executive communications where revision costs are high and quality expectations are exacting. The premium pricing makes sense in these contexts because the cost of poor output (missed deals, legal exposure, damaged credibility) far exceeds the API cost differential.
Skip this feature when you’re building customer support chatbots, generating marketing copy at scale, prototyping conversational interfaces, or handling any task where GPT-4o’s quality is “good enough.” The 15x price multiplier doesn’t deliver 15x value in these scenarios. You’re paying for precision you don’t need.
Real-world use cases where GPT-4.5 justifies its cost
Legal document drafting and review
A mid-sized law firm uses GPT-4.5 to draft contract amendments, review discovery documents, and generate legal memoranda. The model’s 71.4% GPQA science score translates to strong performance on technical legal concepts, and the 63.2% human preference rating means partners spend less time rewriting associate-level work.
The math works here because attorney time costs $300 to $800 per hour. If GPT-4.5 reduces partner review time by even 15 minutes per document, that’s $75 to $200 in saved labor. The API cost for a 5,000-word document with a 2,000-word response is roughly $0.75. The ROI is obvious.
This is for law firms, corporate legal departments, and compliance teams where output quality directly impacts legal risk and client satisfaction. Claude for Healthcare underwent clinical validation before deployment, but no equivalent legal validation exists for GPT-4.5. Firms should treat outputs as first drafts requiring attorney review, not finished work product.
Executive communications and investor relations
A publicly traded company uses GPT-4.5 to draft earnings call scripts, shareholder letters, and SEC filings. The model’s strength in professional writing shows up in tone consistency, argument structure, and technical accuracy. The communications team reports that GPT-4.5 drafts require 30% less editing than GPT-4o drafts.
The 30% editing time reduction matters because these documents go through multiple review cycles involving C-suite executives, legal counsel, and investor relations specialists. Each revision cycle costs hours of highly paid professional time. If GPT-4.5 eliminates one revision cycle, it pays for itself immediately.
This is for investor relations teams, corporate communications departments, and executive assistants supporting C-level leadership. The catch is that these documents often contain forward-looking statements with legal implications. Human oversight remains mandatory regardless of model quality.
Technical whitepaper and research report generation
A technology consulting firm uses GPT-4.5 to generate client-facing research reports on emerging technologies. The 71.4% GPQA score means the model handles complex technical concepts more accurately than GPT-4o, and the professional writing quality means reports feel authoritative rather than AI-generated.
Consultants report that GPT-4.5-generated reports require fact-checking but rarely need structural rewrites. That’s a meaningful efficiency gain in an industry where report quality directly impacts client renewals and upsell opportunities.
This is for consulting firms, research organizations, and enterprise teams producing thought leadership content. Our Sudowrite review found that creative writing quality comes from prompting technique, but technical writing benefits more from base model capabilities.
Enterprise knowledge base synthesis
A Fortune 500 company uses GPT-4.5 to synthesize internal documentation, technical specifications, and project retrospectives into executive summaries. The 128K context window handles large document sets, and the professional writing quality means summaries are presentation-ready.
The use case works because these summaries inform strategic decisions worth millions of dollars. A 5% improvement in summary accuracy or clarity can change project prioritization, resource allocation, or go-to-market timing. The API cost is irrelevant compared to decision impact.
This is for enterprise architecture teams, strategic planning groups, and program management offices. The limitation is that GPT-4.5’s 128K context window lags behind Claude Opus 4’s 200K standard window, so extremely large document sets may require chunking strategies.
Regulatory compliance documentation
A financial services firm uses GPT-4.5 to draft compliance reports, audit responses, and regulatory filings. The model’s strength in formal writing and technical accuracy reduces the risk of ambiguous language that could trigger regulatory scrutiny.
Compliance teams report that GPT-4.5 drafts are more likely to pass internal legal review on first submission compared to GPT-4o drafts. That matters because compliance documentation delays can trigger regulatory penalties or block product launches.
This is for compliance departments, risk management teams, and regulated industries (financial services, healthcare, energy). The warning is that regulatory language evolves constantly. Models trained on historical data may miss recent guidance changes.
Academic paper drafting (with significant caveats)
Researchers use GPT-4.5 to draft literature review sections, methodology descriptions, and discussion sections for academic papers. The 71.4% GPQA score suggests strong performance on scientific concepts, and the professional writing quality means drafts read like human-authored work.
But this use case is ethically fraught. Many journals prohibit AI-generated content or require explicit disclosure. Researchers using GPT-4.5 for drafting must treat outputs as research assistance, not authorship. The model should accelerate writing, not replace the intellectual work of scholarship.
This is for academic researchers, graduate students, and research institutions with clear AI use policies. Our Gauth AI review found that educational effectiveness depends on explanation clarity, and the same principle applies to academic writing.
High-stakes email and correspondence drafting
A venture capital firm uses GPT-4.5 to draft investment decline letters, term sheet explanations, and portfolio company guidance. The model’s professional writing quality means correspondence maintains relationship capital even when delivering bad news.
Partners report that GPT-4.5-drafted emails strike the right balance between directness and empathy more consistently than GPT-4o. That matters in relationship-driven businesses where a poorly worded email can damage years of network building.
This is for venture capital firms, private equity groups, and executive teams managing high-value business relationships. We tested ChatGPT on 127 business emails and found that tone control comes from prompt specificity, but GPT-4.5’s base capabilities reduce the need for extensive prompt engineering.
How to access GPT-4.5 via API
GPT-4.5 is available through OpenAI’s standard API using the gpt-4.5-preview model identifier. You’ll need an OpenAI API key, which requires a paid account with billing set up. The preview designation suggests this is a pre-release version that may see updates or parameter changes.
Use the official OpenAI Python SDK (version 1.0 or later) or the Node.js SDK. The API structure is identical to GPT-4 and GPT-4o, so existing integrations require only a model name change. The endpoint is the standard chat completions endpoint at api.openai.com/v1/chat/completions.
The key parameter to watch is temperature. For professional writing tasks where GPT-4.5 excels, keep temperature between 0.3 and 0.5. Higher temperatures (0.7 to 0.9) introduce more variation but can compromise the formal tone that makes GPT-4.5’s outputs valuable. Max tokens should be set based on your output length needs, with a maximum of 4,096 tokens per response.
The gotcha is rate limits. OpenAI applies tier-based rate limiting, and GPT-4.5’s higher pricing may place it in a more restrictive tier than GPT-4o. Check your account’s rate limits in the OpenAI dashboard before deploying at scale. Enterprise customers can negotiate custom rate limits through OpenAI’s sales team.
Function calling works identically to GPT-4, so you can integrate GPT-4.5 into agentic workflows or tool-use scenarios. The model supports the same function calling syntax and JSON mode as other GPT models. For actual code examples and detailed SDK documentation, check OpenAI’s official API reference.
Prompting strategies that maximize GPT-4.5’s professional writing edge
GPT-4.5’s strength is professional task quality, which means your prompts should emphasize structure, tone, and argumentation over creativity or conversational warmth. The model responds well to explicit instructions about audience, purpose, and format. A prompt like “write an executive summary of this technical report for a non-technical board of directors” will produce better results than “summarize this report.”
System prompts should establish professional context and constraints. For legal writing, use a system prompt like “You are a legal analyst drafting formal documents for attorney review. Prioritize precision, cite relevant precedents when applicable, and flag ambiguous areas requiring human judgment.” This anchors the model’s outputs in the professional domain where it excels.
Temperature tuning matters more for GPT-4.5 than for conversational models. For formal documents (contracts, regulatory filings, technical specifications), use temperature 0.3 to 0.4. This reduces variation and keeps outputs consistent with professional norms. For creative professional writing (marketing whitepapers, thought leadership articles), temperature 0.6 to 0.7 allows more stylistic variation while maintaining quality.
Multi-turn conversations work well for iterative refinement. Start with a broad request, then use follow-up prompts to adjust tone, add detail, or restructure arguments. A pattern like “draft a shareholder letter announcing Q3 results” followed by “make the tone more optimistic about future growth” followed by “add a paragraph addressing supply chain concerns” lets you guide the model toward your exact requirements.
What doesn’t work: asking GPT-4.5 to “be more empathetic” or “sound more human.” The model’s training optimizes for professional quality, not emotional resonance. If you need warmth or conversational tone, GPT-4o is a better (and much cheaper) choice. GPT-4.5 is for formal contexts where precision and authority matter more than relatability.
Avoid over-specifying format in prompts. Instead of “write a five-paragraph essay with an introduction, three body paragraphs, and a conclusion,” say “write a structured analysis with clear sections.” GPT-4.5’s strength is professional judgment about structure. Let it make those decisions rather than forcing artificial constraints.
For technical writing, provide context documents in the prompt when possible. GPT-4.5’s 128K context window can handle substantial reference material. A prompt like “using the attached technical specifications, draft a whitepaper explaining this technology for enterprise buyers” will produce more accurate outputs than asking the model to work from its training data alone.
GPT-4.5’s limitations are deal-breakers for most developers
The pricing is absurd for anything except high-stakes professional writing. At $75/$150 per million tokens, GPT-4.5 costs 15 times more than GPT-4o and 5 times more than Claude Opus 4. A typical chatbot handling 10,000 conversations per day would cost $2,600 per day on GPT-4.5 versus $170 on GPT-4o. That’s $78,000 per month versus $5,100. The quality improvement doesn’t justify that differential for conversational AI.
The 128K context window is no longer competitive. Claude Opus 4 offers 200K tokens standard and 1 million tokens in preview. Gemini 1.5 Pro offers 1 million tokens production-ready. For long-document analysis, multi-file codebases, or extended research tasks, GPT-4.5 simply can’t compete. You’ll hit context limits that force chunking strategies or multiple API calls.
Coding performance is worse than GPT-4o despite the higher price. GPT-4o scores 90.2% on HumanEval versus GPT-4.5’s 88.6%. And o3-mini crushes both of them on mathematical reasoning (87.3% on AIME versus GPT-4.5’s 36.7%). If your use case involves code generation, algorithm design, or mathematical problem-solving, GPT-4.5 is the wrong choice.
No multimodal capabilities means GPT-4.5 can’t process images, analyze screenshots, transcribe audio, or handle any task that requires vision or speech input. GPT-4o ships with these capabilities at a fraction of the cost. The regression from multimodal to text-only is baffling and severely limits GPT-4.5’s applicability.
No published SWE-bench scores means we can’t evaluate real-world coding performance. OpenAI published MMLU, HumanEval, GPQA, SimpleQA, and AIME scores but conspicuously omitted SWE-bench. Claude Opus 4 scores 72.5% on SWE-bench Verified. The absence of comparable data for GPT-4.5 suggests the results aren’t competitive.
The “preview” designation in the API endpoint (gpt-4.5-preview) means this model may see breaking changes, parameter adjustments, or behavioral shifts without notice. Production deployments carry higher risk than stable model versions. OpenAI’s track record with preview models is mixed. Some graduate to stable releases quickly, others languish in preview for months.
Security posture and compliance considerations
GPT-4.5 operates under OpenAI’s standard security framework: TLS 1.3 encryption in transit, AES-256 encryption at rest, and 30-day data retention for abuse monitoring. Enterprise customers can opt into zero-retention agreements that delete API data immediately after processing. This matches the security posture of GPT-4 and GPT-4o.
Compliance certifications include SOC 2 Type II, GDPR compliance for EU customers, and HIPAA eligibility for Business Associate Agreement (BAA) customers. Financial services firms should note that OpenAI’s infrastructure is US-based, which may complicate data residency requirements in some jurisdictions. Claude offers EU data residency options that GPT-4.5 doesn’t match.
The OpenAI Moderation API filters harmful content (violence, hate speech, sexual content, self-harm) before processing requests. This is a mandatory layer that can’t be disabled. For legal and compliance use cases, this moderation may occasionally flag legitimate content (discussion of violent crimes in legal briefs, medical terminology in healthcare documents). Test your specific use cases to confirm moderation doesn’t block valid inputs.
A unique risk for GPT-4.5 is its optimization for professional writing quality. The model is trained to produce authoritative, well-structured outputs. In adversarial scenarios (social engineering, phishing, fraud), this capability could be weaponized to create more convincing malicious content. Organizations deploying GPT-4.5 should implement additional output validation for customer-facing applications.
For regulated industries, the lack of model transparency is a problem. OpenAI doesn’t disclose training data sources, parameter counts, or architectural details. This makes regulatory compliance audits difficult. Financial institutions subject to model risk management requirements may struggle to document GPT-4.5’s behavior in audit-ready formats.
Version history and model evolution
| Date | Version | Key Changes |
|---|---|---|
| February 27, 2025 | gpt-4.5-preview | Initial release with 128K context, professional writing focus, $75/$150 pricing |
| May 13, 2024 | gpt-4o | Multimodal predecessor with vision/voice, 128K context, $5/$15 pricing |
| March 14, 2023 | gpt-4 | Original GPT-4 release with 8K/32K context options, $30/$60 pricing |
| November 30, 2022 | gpt-3.5-turbo | ChatGPT launch model, 4K context, $0.50/$1.50 pricing |
The GPT-4.5 release breaks the pattern of OpenAI’s previous model updates. GPT-4o added multimodal capabilities and reduced pricing. GPT-4 Turbo increased context windows and improved speed. GPT-4.5 strips out multimodal features, keeps the same context window, and dramatically increases pricing. It’s an outlier in the product line.
No updates have been announced since the February 27, 2025 launch. The “preview” designation typically indicates OpenAI plans iterative improvements, but the company hasn’t published a roadmap or timeline for a stable release. Based on previous preview models, expect 3 to 6 months before either a stable version ships or OpenAI quietly deprecates the experiment.
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The broader context for GPT-4.5’s positioning becomes clearer when you look at OpenAI’s GPT-5 development, which signals the company’s actual strategic priorities. While GPT-4.5 focuses on professional writing quality, GPT-5 aims for multimodal reasoning and agentic capabilities that compete directly with Claude Opus 4.
For teams evaluating whether to invest in GPT-4.5 integration, our ChatGPT vs Claude comparison provides decision frameworks based on use case, budget, and performance requirements. The analysis shows that Claude wins for coding and long-horizon tasks while ChatGPT (via GPT-4o) wins for cost-effective general use.
The professional writing use case that justifies GPT-4.5’s pricing has parallels in creative writing tools. Our Sudowrite review across 70,000 words found that specialized writing tools don’t always outperform general-purpose models, which raises questions about whether GPT-4.5’s narrow advantage justifies its premium.
For developers building chatbots or conversational AI, our 2026 chatbot testing across five leading platforms found that user satisfaction correlates more strongly with response accuracy and speed than with subjective quality metrics. That data suggests GPT-4.5’s professional writing edge may not translate to better user experiences in conversational contexts.
Understanding how LLMs work at a technical level helps contextualize GPT-4.5’s tradeoffs. The architectural changes that improve professional writing quality likely come at the cost of reasoning depth and multimodal capabilities, which explains why GPT-4.5 underperforms on coding and math benchmarks.
For organizations considering GPT-4.5 for customer-facing applications, our CrushOn AI review demonstrates that character consistency and emotional memory can be implemented at the application layer rather than requiring model-level capabilities. That suggests GPT-4.5’s professional writing quality could be approximated with GPT-4o plus better prompt engineering.
The competitive landscape for AI models in 2026 is documented in our ranking of ChatGPT alternatives, which found that Claude and Gemini offer better value for most use cases. GPT-4.5’s narrow advantage in professional writing doesn’t change that fundamental value equation for the majority of developers.
Common questions about GPT-4.5
What is GPT-4.5?
GPT-4.5 is OpenAI’s text-only language model released February 27, 2025, optimized for professional writing tasks. It costs $75 per million input tokens and $150 per million output tokens, roughly 15 times more expensive than GPT-4o. The model scores 89.6% on MMLU benchmarks and achieves a 63.2% human preference win rate over GPT-4o on professional tasks. It’s available via the gpt-4.5-preview API endpoint.
When was GPT-4.5 released?
GPT-4.5 launched on February 27, 2025 as a preview model. OpenAI announced the release with benchmark data but limited technical documentation. The preview designation suggests this is a pre-release version that may see updates before a stable release. No timeline has been published for when the model will exit preview status.
How much does GPT-4.5 cost?
GPT-4.5 costs $75 per million input tokens and $150 per million output tokens. For comparison, GPT-4o costs $5/$15, Claude Opus 4 costs $15/$75, and o3-mini costs $1.10/$4.40. A typical 2,000-word conversation with a 500-word response costs about $0.26 on GPT-4.5 versus $0.017 on GPT-4o. The pricing makes GPT-4.5 viable only for high-value professional writing tasks.
Is GPT-4.5 better than GPT-4o?
GPT-4.5 is better than GPT-4o for professional writing tasks (63.2% human preference win rate) and scientific knowledge questions (71.4% GPQA versus 53.6%). But GPT-4o is better for coding (90.2% HumanEval versus 88.6%), costs 15 times less, and includes vision and voice capabilities that GPT-4.5 lacks. For most use cases, GPT-4o is the better choice. GPT-4.5 only makes sense for high-stakes professional documents where quality justifies the cost premium.
Does GPT-4.5 have vision or voice capabilities?
No. GPT-4.5 is text-only. It cannot process images, analyze screenshots, transcribe audio, or handle any multimodal input. This is a regression from GPT-4o, which includes vision and voice. If your use case requires multimodal capabilities, you need GPT-4o or a competitor like Claude Opus 4 (which includes vision).
What is GPT-4.5’s context window?
GPT-4.5 has a 128,000 token context window, the same as GPT-4o and GPT-4 Turbo. This handles roughly 96,000 words of input. But it’s not competitive with Claude Opus 4’s 200,000 token standard window or 1 million token preview access. For long-document analysis or multi-file processing, Claude offers more capacity.
Can I use GPT-4.5 for coding?
You can, but you shouldn’t. GPT-4.5 scores 88.6% on HumanEval coding tasks, worse than GPT-4o’s 90.2%. It scores 36.7% on AIME mathematical reasoning, dramatically worse than o3-mini’s 87.3%. OpenAI hasn’t published SWE-bench scores for GPT-4.5, which suggests they’re not competitive. For coding tasks, use GPT-4o, Claude Opus 4, or o3-mini.
Is GPT-4.5 available for free?
No. GPT-4.5 is API-only and requires a paid OpenAI account with billing configured. It’s not available through ChatGPT’s free tier or any free access methods. The pricing ($75/$150 per million tokens) is among the highest in the industry, making it unsuitable for experimentation or low-budget projects. For free AI access, use ChatGPT’s free tier (which uses GPT-3.5 or limited GPT-4o) or open-source alternatives.