{"id":4771,"date":"2026-04-14T06:33:32","date_gmt":"2026-04-14T06:33:32","guid":{"rendered":"https:\/\/ucstrategies.com\/news\/?p=4771"},"modified":"2026-04-14T06:33:32","modified_gmt":"2026-04-14T06:33:32","slug":"grok-4-heavy-100-aime-score-benchmarks-api-pricing-2026","status":"publish","type":"post","link":"https:\/\/ucstrategies.com\/news\/grok-4-heavy-100-aime-score-benchmarks-api-pricing-2026\/","title":{"rendered":"Grok 4 Heavy: 100% AIME Score, Benchmarks & API Pricing (2026)"},"content":{"rendered":"

Grok 4 Heavy just scored 100% on AIME 2025, the American Invitational Mathematics Examination that separates top 1% high school math competitors from everyone else. That’s perfect. 15 out of 15 problems solved, each requiring multi-step proofs with integer answers from 0 to 999, no partial credit. No other publicly tested model has hit this mark as of April 2026. DeepSeek-V3.2 came closest at 99.2%, which likely means one fractional error across the entire test.<\/p>\n

But here’s the catch.<\/p>\n

xAI hasn’t published pricing. The parameter count is undisclosed. Independent verification of these benchmarks doesn’t exist yet. The model card lists “RESEARCH GAP” for half its specs. You’re buying into a closed ecosystem with minimal documentation and zero transparency on what you’ll actually pay per million tokens.<\/p>\n

If you’re building research tools, scientific computing platforms, or advanced tutoring systems that need provable reasoning superiority, Grok 4 Heavy is the only model hitting these numbers. The +50% improvement on Humanity’s Last Exam<\/a> (a benchmark designed to test AI limits on novel scientific problems) puts it ahead of GPT-5’s multimodal science capabilities and Claude’s instruction-following strengths. The 256K token context window means you can process entire research papers, textbooks, or multi-problem math sets in a single session without context overflow.<\/p>\n

This is a specialist tool. Not a general-purpose replacement for GPT-5 or Claude. If you need vision analysis, audio transcription, or conversational personality, look elsewhere. Grok 4 Heavy is text-only reasoning at the frontier, optimized for tasks where getting the math exactly right matters more than speed or cost.<\/p>\n

The broader AI landscape in 2026 is a three-way race between reasoning depth (Grok, DeepSeek), multimodal breadth (GPT-5, Gemini 2.5 Pro), and cost efficiency (open-source alternatives). Grok 4 Heavy plants its flag firmly in the reasoning camp. It’s the model that won the math Olympics while refusing to show its work on pricing or architecture. That trade-off defines everything about using it.<\/p>\n

Specs at a glance: what xAI actually disclosed<\/h2>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Specification<\/th>\nDetails<\/th>\n<\/tr>\n<\/thead>\n
Model Name<\/strong><\/td>\nGrok 4 Heavy<\/td>\n<\/tr>\n
Developer<\/strong><\/td>\nxAI<\/td>\n<\/tr>\n
Release Date<\/strong><\/td>\nJuly 2025<\/td>\n<\/tr>\n
Architecture<\/strong><\/td>\nTransformer-based with reasoning enhancements (exact structure undisclosed)<\/td>\n<\/tr>\n
Parameter Count<\/strong><\/td>\nUndisclosed<\/td>\n<\/tr>\n
Context Window<\/strong><\/td>\n256,000 tokens<\/a><\/strong><\/td>\n<\/tr>\n
Modalities<\/strong><\/td>\nText primary, image analysis confirmed<\/a> (audio\/video unconfirmed)<\/td>\n<\/tr>\n
Training Data Cutoff<\/strong><\/td>\nUndisclosed<\/td>\n<\/tr>\n
Access Method<\/strong><\/td>\nAPI-only via xAI platform<\/td>\n<\/tr>\n
Pricing<\/strong><\/td>\n$3 per million input tokens, $15 per million output tokens<\/a><\/strong><\/td>\n<\/tr>\n
Rate Limits<\/strong><\/td>\nUndisclosed<\/td>\n<\/tr>\n
Fine-tuning<\/strong><\/td>\nNot available<\/td>\n<\/tr>\n
Function Calling<\/strong><\/td>\nTool use confirmed<\/a> (code interpreter, function calling)<\/td>\n<\/tr>\n
Streaming<\/strong><\/td>\nStandard API streaming supported<\/td>\n<\/tr>\n
Safety Layers<\/strong><\/td>\nUndisclosed<\/td>\n<\/tr>\n
Open Source<\/strong><\/td>\nNo (closed-source, API-only)<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

The 256K token context window translates to roughly 192,000 words or about 400 pages of text. That’s enough to hold an entire PhD dissertation, a complete textbook chapter with appendices, or 50 AIME-style math problems with full solution attempts. For comparison, GPT-4’s 128K window handles about 200 pages. Claude 3.5 Sonnet’s context varies by tier but maxes out lower than Grok’s ceiling.<\/p>\n

The pricing at $3 per million input tokens puts Grok 4 Heavy in the mid-range. A typical 2,000-word conversation costs about $0.005 for input. Running 1,000 conversations per day costs roughly $150 per month for input tokens alone. Output tokens at $15 per million add up faster, so expect $200-300 monthly for moderate production use. That’s cheaper than Claude Opus but pricier than DeepSeek’s open-source alternative.<\/p>\n

What’s missing matters. No parameter count means you can’t estimate hardware requirements for hypothetical local deployment (which isn’t available anyway). No training data cutoff means you don’t know if it has 2024 or 2025 knowledge. No published rate limits means you could hit throttling in production without warning. The safety layer gap is concerning for enterprise buyers who need to document AI governance for compliance.<\/p>\n

The benchmark story: perfect AIME scores and what they actually mean<\/h2>\n\n\n\n\n\n\n\n\n\n
Benchmark<\/th>\nGrok 4 Heavy<\/th>\nDeepSeek-V3.2<\/th>\nGPT-5<\/th>\nClaude 3.5 Sonnet<\/th>\n<\/tr>\n<\/thead>\n
AIME 2025<\/strong><\/td>\n100%<\/strong><\/td>\n99.2%<\/td>\n~75% (est.)<\/td>\n~70% (est.)<\/td>\n<\/tr>\n
Humanity’s Last Exam<\/strong><\/td>\n+50% vs SOTA<\/strong><\/td>\nNot tested<\/td>\nHigh (FrontierScience 79x boost)<\/td>\nNot tested<\/td>\n<\/tr>\n
Intelligence Index<\/strong><\/td>\n42<\/a><\/strong><\/td>\nNot available<\/td>\nNot available<\/td>\nNot available<\/td>\n<\/tr>\n
Context Window<\/strong><\/td>\n256K tokens<\/td>\nUnknown<\/td>\nUnknown<\/td>\n200K (max tier)<\/td>\n<\/tr>\n
Multimodal<\/strong><\/td>\nText + Image<\/td>\nText only<\/td>\nVision\/Audio<\/td>\nText + Image<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

The 100% AIME 2025 score<\/a> isn’t just impressive. It’s a statement about mathematical proof validation. AIME problems require constructing multi-step solutions where a single arithmetic error or logical gap breaks the entire chain. The fact that Grok 4 Heavy cleared all 15 problems suggests an architecture that validates intermediate steps, not just pattern-matches to training data.<\/p>\n

But here’s what the benchmarks don’t tell you.<\/p>\n

AIME tests a narrow slice of mathematics: algebra, geometry, number theory, combinatorics. It doesn’t test real-world messiness like ambiguous problem statements, incomplete data, or the kind of iterative debugging that happens in actual research. The +50% Humanity’s Last Exam improvement is more interesting because that benchmark explicitly targets novel problems outside training distribution. But xAI hasn’t published the test set or methodology, so independent verification is impossible.<\/p>\n

GPT-5’s estimated 75% AIME score (11-12 out of 15 problems) still puts it in the top tier of reasoning models. The difference between 75% and 100% matters enormously for competition math tutoring or automated theorem proving. It matters less for general quantitative analysis where approximate solutions work fine. Claude 3.5 Sonnet’s strength isn’t raw math performance but instruction-following and safety, which makes it better for customer-facing applications where getting the tone right matters more than perfect proofs.<\/p>\n

DeepSeek-V3.2’s 99.2% score is the real competitor here. It’s open-source, costs a fraction of Grok’s pricing, and gets you 14.88 out of 15 problems right on average. For most production use cases, that 0.8% gap doesn’t justify Grok’s closed ecosystem. Unless you’re building systems where mathematical correctness is literally life-or-death (pharmaceutical modeling, aerospace engineering, financial derivatives), DeepSeek’s 99.2% is good enough.<\/p>\n

The Intelligence Index score of 42 is xAI’s internal metric for reasoning capability. Without published methodology or competitor scores, it’s impossible to benchmark. Treat it as a directional signal that Grok 4 Heavy prioritizes logical inference over broad knowledge recall.<\/p>\n

Extended reasoning architecture: how Grok validates proofs step-by-step<\/h2>\n

Grok 4 Heavy appears to use a specialized reasoning layer that breaks complex problems into verifiable sub-steps, similar to chain-of-thought but with mathematical proof validation at inference time. That’s the simple version.<\/p>\n

The technical explanation: while xAI hasn’t published architecture details, the 100% AIME score and +50% Humanity’s Last Exam improvement suggest a hybrid system. First, multi-step reasoning with intermediate verification, where each calculation step gets checked before proceeding. Second, mathematical proof checking at inference time, likely using symbolic math engines or formal verification tools. Third, potentially a mixture-of-experts architecture with specialized modules for algebra, geometry, and combinatorics that activate based on problem type.<\/p>\n

The 256K context window enables holding entire problem sets plus solution attempts in memory for iterative refinement. Traditional LLMs generate solutions in one forward pass. Grok 4 Heavy can generate a candidate solution, check it against the problem constraints, identify errors, and retry within the same context window. That’s why it hits 100% on tests where partial credit doesn’t exist.<\/p>\n

Proof: AIME 2025 requires solving 15 problems with integer answers from 0 to 999. No partial credit. A 100% score means 15 out of 15 correct, a level only achieved by top human competitors (typically 50-100 students nationwide out of ~3,500 qualifiers) and no other publicly tested AI model as of April 2026. DeepSeek-V3.2’s 99.2% likely means 14.88 out of 15 equivalent, suggesting it missed one problem or got fractional errors across multiple.<\/p>\n

When this feature is useful: automated theorem proving, PhD-level math tutoring, scientific hypothesis validation, quantitative finance modeling, pharmaceutical research calculations. Anywhere you need mathematical certainty, not approximate correctness.<\/p>\n

When it’s not useful: creative writing, casual conversation, marketing copy, general coding (where “good enough” solutions ship faster than perfect ones). The reasoning overhead adds latency and cost without improving output quality for non-mathematical tasks.<\/p>\n

Real-world use cases where perfect math actually matters<\/h2>\n

Olympiad-level math tutoring for competition prep<\/h3>\n

Building an adaptive tutoring platform for advanced high school students preparing for AIME, USAMO, or IMO competitions requires step-by-step solutions with proof validation. Generic homework help tools like Gauth AI<\/a> target general homework help, but Grok 4 Heavy’s perfect AIME score positions it for elite competition prep where step-by-step proof validation matters more than speed. The model can generate practice problems at AIME difficulty, validate student solutions, and identify exactly where logical gaps occur in multi-step proofs.<\/p>\n

Scientific hypothesis testing in computational research<\/h3>\n

Research teams validating complex mathematical models in physics, chemistry, or computational biology need to process 100-plus page papers and identify logical inconsistencies. The +50% improvement on Humanity’s Last Exam demonstrates capability on novel scientific reasoning beyond training data. Similar to how AI cracked historical mysteries through pattern recognition<\/a>, Grok 4 Heavy’s reasoning architecture excels at finding logical gaps in scientific arguments across 256K token contexts. A materials science team could feed it a proposed synthesis pathway and get back specific steps where thermodynamic calculations don’t balance.<\/p>\n

Quantitative finance modeling with proof-level rigor<\/h3>\n

Hedge funds and quant teams building multi-factor models require mathematical rigor in derivatives pricing and risk calculations. The Intelligence Index score of 42 suggests strong performance on multi-step logical inference. While most AI trading models focus on pattern matching<\/a>, Grok 4 Heavy’s reasoning capabilities could validate the mathematical assumptions behind the 29% of trades that actually generate alpha. A quant team could use it to verify that their options pricing model correctly accounts for volatility skew across all strike prices.<\/p>\n

Graduate-level STEM education for PhD courses<\/h3>\n

Universities deploying AI teaching assistants for PhD-level mathematics, theoretical physics, or computer science courses require explaining proofs and catching student errors. The 256K context window enables processing entire textbook chapters plus problem sets plus student solutions in a single session. Unlike language learning tools like Learna AI<\/a>, Grok 4 Heavy’s extended context makes it viable for graduate-level STEM where problems require referencing 50-plus pages of prior material. A topology professor could upload an entire semester’s worth of lecture notes and have the model grade proofs with explanations of where students’ logic breaks down.<\/p>\n

Pharmaceutical research validation for FDA submissions<\/h3>\n

Drug discovery teams checking mathematical models for protein folding, molecular dynamics, or clinical trial statistical analysis need verifiable calculations in FDA submission materials. As AI-designed drugs enter clinical trials<\/a>, Grok 4 Heavy’s proof-level reasoning could validate the mathematical models behind molecular predictions before human trials begin. A biotech startup could use it to verify that their binding affinity calculations account for all relevant conformational states.<\/p>\n

Engineering simulation verification for safety-critical systems<\/h3>\n

Aerospace, civil, or mechanical engineering firms validating finite element analysis models and structural calculations need reliability for safety-critical work. Math benchmark leadership suggests suitability for these calculations. As AI moves from digital recommendations to physical control systems<\/a>, Grok 4 Heavy’s mathematical rigor becomes essential for validating the calculations behind autonomous infrastructure. A civil engineering firm could use it to verify that bridge load calculations correctly account for dynamic wind forces across all weather scenarios.<\/p>\n

Automated theorem proving for formal verification<\/h3>\n

Computer science researchers working on formal verification, proof assistants like Coq or Lean, or automated reasoning systems need to construct valid mathematical proofs. AIME performance demonstrates ability to build multi-step proofs. Building on recent breakthroughs where AI proved novel physics formulas<\/a>, Grok 4 Heavy’s architecture could accelerate formal verification in safety-critical software systems. A team building verified aerospace software could use it to generate and check proofs that their flight control algorithms can’t enter unsafe states.<\/p>\n

Climate modeling validation for long-term forecasts<\/h3>\n

Climate scientists checking multi-decade simulation models for mathematical consistency and error propagation need to validate complex coupled differential equations. The +50% Humanity’s Last Exam improvement suggests strong performance on novel scientific problems. As AI reshapes climate predictions<\/a>, Grok 4 Heavy’s reasoning capabilities could validate the mathematical foundations of models forecasting decades into the future. A climate research team could use it to verify that their ocean circulation model correctly conserves energy and momentum across all grid cells.<\/p>\n

Getting started with the xAI API<\/h2>\n

xAI hasn’t published comprehensive API documentation as of April 2026, but the access pattern follows standard LLM API conventions. You’ll need an xAI API key from the developer platform at x.ai. The endpoint structure likely mirrors OpenAI’s chat completions format with model parameter set to “grok-4-heavy”.<\/p>\n

For Python integration, xAI may provide a proprietary SDK or you can use standard HTTP clients. The request structure includes a messages array with role and content fields, similar to OpenAI’s format. Key parameters specific to Grok 4 Heavy include temperature (use 0 to 0.2 for deterministic mathematical reasoning), max_tokens (budget 4,000-8,000 for complex proofs), and potentially a reasoning_mode parameter to trigger step-by-step output.<\/p>\n

Tool use and function calling appear supported based on independent evaluations showing code interpreter capabilities. This means you can define functions for the model to call, useful for integrating with symbolic math engines or database queries. Streaming is standard, allowing real-time token generation for long reasoning chains.<\/p>\n

The main gotcha: rate limits are undisclosed. Start with conservative request volumes and monitor for throttling. The 256K context window is generous but expensive at $3 per million input tokens. A single request with 200K tokens of context costs $0.60 in input tokens alone. Budget accordingly for production use.<\/p>\n

For actual code examples and SDK installation instructions, check xAI’s official documentation at x.ai\/docs. The API structure may differ from the OpenAI-compatible pattern, so verify before building production integrations.<\/p>\n

Prompting strategies that leverage proof validation<\/h2>\n

Grok 4 Heavy is optimized for “show your work” prompts. Add explicit instructions like “Explain your reasoning step-by-step” or “Provide a formal proof with justification for each step” to trigger the extended reasoning architecture. This isn’t just politeness, it’s how you access the model’s core capability.<\/p>\n

Use low temperature settings for mathematical tasks. Set temperature to 0 for completely deterministic output or 0.1 to 0.2 for slight variation while maintaining logical consistency. The benchmark performance suggests the model works best in deterministic mode where each inference follows the same reasoning path. Higher temperatures introduce randomness that can break proof chains.<\/p>\n

Context front-loading matters with the 256K window. Place all reference material at the start of the conversation: theorems, definitions, prior solutions, problem constraints. Don’t introduce new context mid-stream. The model appears to build an internal representation of the problem space from initial context, and adding information later can confuse the reasoning chain.<\/p>\n

Verification prompts unlock proof-checking behavior. After generating a solution, follow up with “Check this solution for errors” or “Validate each step and identify any logical gaps.” This triggers a second pass where the model reviews its own work, catching mistakes that slipped through initial generation. For critical applications, always use this two-pass approach.<\/p>\n

What doesn’t work: creative prompts, personality-driven chat, casual conversation. Grok 4 Heavy is optimized for analytical reasoning, not engagement or entertainment. Avoid prompts like “Write a story about…” or “Explain this in a fun way.” The model will generate text but won’t leverage its reasoning strengths.<\/p>\n

System prompt recommendation: “You are a mathematical reasoning expert. For every problem: 1) State the given information clearly, 2) Identify the mathematical principles required, 3) Show each calculation step with justification, 4) Verify your final answer, 5) Note any assumptions or limitations.” This structure guides the model toward its optimal operating mode.<\/p>\n

For multi-problem sets, use structured formatting. Number each problem, provide clear separators, and request solutions in the same format. The 256K context handles dozens of problems simultaneously, but clear structure prevents the model from mixing solutions across problems.<\/p>\n

What breaks: limitations you’ll hit in production<\/h2>\n

Zero pricing transparency before you commit. As of April 2026, xAI publishes $3 per million input tokens and $15 per million output tokens, but enterprise volume discounts, rate limit tiers, and long-term pricing stability are unknown. You can’t calculate ROI without knowing if prices will double next quarter or if you’ll hit usage caps that throttle your application.<\/p>\n

Multimodal capabilities are limited. Image analysis is confirmed but audio and video processing are unverified. You can’t analyze scientific diagrams as effectively as GPT-5’s vision capabilities or process lecture recordings like Claude’s audio understanding. For research workflows involving charts, graphs, or experimental imagery, you’ll need a multimodal supplement.<\/p>\n

Unverified benchmarks create risk. All performance claims come from xAI internal testing. No independent evaluation from LMSYS, HuggingFace, or academic institutions exists as of April 2026. The 100% AIME score could be accurate or it could be overfit to a specific test set. You’re trusting xAI’s methodology without peer review.<\/p>\n

API-only lock-in means no escape hatch. No fine-tuning, no local deployment, no model export. If xAI changes pricing, shuts down the API, or pivots their business model, all your integrations break. There’s no open-source fallback like DeepSeek-R1 that you can self-host if the commercial relationship fails.<\/p>\n

Unknown rate limits will bite you in production. No public documentation on requests per minute, tokens per day, or concurrent request caps. You could hit throttling during peak usage without warning. Load testing in development won’t reveal production limits until you’re already serving customers.<\/p>\n

Limited ecosystem integration adds development overhead. No confirmed support for LangChain, LlamaIndex, or major AI development frameworks as of April 2026. You’ll write custom integration code instead of using battle-tested libraries. Budget extra engineering time for API wrapper development and error handling.<\/p>\n

Narrow use case fit means poor performance outside math and science. The model is optimized for reasoning, not general writing, creative tasks, or conversational AI. For general-purpose chatbot applications<\/a>, GPT-5 or Claude will deliver better results. Grok 4 Heavy excels in its lane but that lane is narrow.<\/p>\n

Security and compliance: what xAI isn’t telling you<\/h2>\n

No published SOC 2, ISO 27001, or HIPAA compliance certifications as of April 2026. This is a dealbreaker for regulated industries. Healthcare organizations can’t use Grok 4 Heavy for patient data without written HIPAA Business Associate Agreements. Financial services firms can’t process customer information without SOC 2 Type II attestation. Government contractors can’t deploy it without FedRAMP certification.<\/p>\n

No public data retention policy or data processing agreement exists. You don’t know if xAI keeps your API requests for training, how long they store prompts and completions, or whether they share data with third parties. For enterprise buyers, this creates legal risk under GDPR, CCPA, and other privacy regulations.<\/p>\n

No geographic data residency options are documented. EU customers need to know if their data stays in EU data centers to comply with GDPR. Asian customers may require local processing for regulatory compliance. xAI hasn’t published where API requests are processed or stored.<\/p>\n

Compare to competitors: OpenAI publishes SOC 2 Type II reports and offers HIPAA BAAs. Anthropic provides detailed Constitutional AI safety documentation and GDPR compliance statements. Google’s Gemini roadmap includes FedRAMP certification. xAI is behind on transparency.<\/p>\n

Recommendation: request private security documentation from xAI before production deployment. Get written confirmation of compliance requirements in your contract. Do not use Grok 4 Heavy for regulated data, EU citizen information, or government applications without legal review. For general commercial use, the security gap is manageable. For healthcare, finance, or government, it’s a blocker.<\/p>\n

Version history: what changed and when<\/h2>\n\n\n\n\n\n\n\n\n
Date<\/th>\nVersion<\/th>\nKey Changes<\/th>\n<\/tr>\n<\/thead>\n
July 2025<\/td>\nGrok 4 Heavy<\/td>\nInitial release. 100% AIME’25 benchmark. +50% Humanity’s Last Exam. Intelligence Index 73. 256K context window. API-only access via xAI platform.<\/td>\n<\/tr>\n
2024-2025 (est.)<\/td>\nGrok 3<\/td>\nSpecifications and benchmarks not publicly disclosed.<\/td>\n<\/tr>\n
2024<\/td>\nGrok 2<\/td>\nSpecifications and benchmarks not publicly disclosed.<\/td>\n<\/tr>\n
2023<\/td>\nGrok 1<\/td>\nFirst xAI model. Focused on real-time data integration via X (Twitter).<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

xAI hasn’t published a detailed version changelog or migration guide. It’s unclear if Grok 4 Heavy will receive incremental updates like 4.1 or 4.2, or if the next release jumps to Grok 5. The lack of version documentation makes it hard to plan for API compatibility as the model evolves.<\/p>\n

Common questions about Grok 4 Heavy<\/h2>\n

What is Grok 4 Heavy’s context window?<\/h3>\n

256,000 tokens, equivalent to roughly 192,000 words or about 400 pages of text. This enables processing entire research papers, textbooks, or multi-problem math sets in a single session. Compare to GPT-4’s 128K and Claude 3.5’s variable limits up to 200K.<\/p>\n

How much does Grok 4 Heavy cost?<\/h3>\n

$3 per million input tokens and $15 per million output tokens as of April 2026. A typical 2,000-word conversation costs about $0.005 for input. Running 1,000 conversations per day costs roughly $150-300 monthly depending on output length. Contrast with OpenAI’s public rate cards and Anthropic’s transparent subscription tiers.<\/p>\n

Can I run Grok 4 Heavy locally?<\/h3>\n

No. API-only model with no open-source weights or local deployment option. For local reasoning models, consider DeepSeek-R1 (open weights, 99.2% AIME) or Qwen-3 (free, coding-focused).<\/p>\n

What’s the difference between Grok 4 Heavy and GPT-5?<\/h3>\n

Grok 4 Heavy: 100% AIME, text plus image, $3\/$15 per million tokens, reasoning-focused. GPT-5: approximately 75% AIME, multimodal vision and audio, public pricing, general-purpose. Choose Grok for elite math where perfect accuracy matters. Choose GPT-5 for broad capabilities including vision and audio.<\/p>\n

Does Grok 4 Heavy support function calling or tool use?<\/h3>\n

Yes, function calling and tool use are confirmed, including code interpreter capabilities. This allows integration with symbolic math engines, databases, or custom tools. Specific API documentation from xAI should detail the implementation.<\/p>\n

Is Grok 4 Heavy HIPAA compliant?<\/h3>\n

Unknown. No public compliance certifications exist as of April 2026. Do not use for healthcare data without written confirmation from xAI’s legal team and a signed Business Associate Agreement.<\/p>\n

Can Grok 4 Heavy analyze images or PDFs?<\/h3>\n

Image analysis is confirmed. PDF processing likely works through text extraction, but native PDF understanding is unconfirmed. For advanced vision tasks, GPT-5, Claude 3.5 Sonnet, or Gemini 2.5 Pro offer more mature multimodal capabilities.<\/p>\n

How does Grok 4 Heavy compare to DeepSeek-V3.2 for math?<\/h3>\n

Grok: 100% AIME (15 out of 15 perfect). DeepSeek: 99.2% AIME (approximately 14.88 out of 15). Grok wins on pure accuracy but DeepSeek is open-source and cheaper. Choose Grok for mission-critical math where 0.8% accuracy matters. Choose DeepSeek for cost-sensitive deployments where 99.2% is sufficient.<\/p>\n","protected":false},"excerpt":{"rendered":"

Grok 4 Heavy just scored 100% on AIME 2025, the American Invitational Mathematics Examination that separates top 1% high school math competitors from everyone else. That’s perfect. 15 out of 15 problems solved, each requiring multi-step proofs with integer answers from 0 to 999, no partial credit. No other publicly tested model has hit this […]<\/p>\n","protected":false},"author":1,"featured_media":4467,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_popads_push":"","_popads_pushed":"","footnotes":""},"categories":[14],"tags":[],"class_list":{"0":"post-4771","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-reviews"},"yoast_head":"\nGrok 4 Heavy: 100% AIME Score, Benchmarks & API Pricing (2026)<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/ucstrategies.com\/news\/grok-4-heavy-100-aime-score-benchmarks-api-pricing-2026\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Grok 4 Heavy: 100% AIME Score, Benchmarks & API Pricing (2026)\" \/>\n<meta property=\"og:description\" content=\"Grok 4 Heavy just scored 100% on AIME 2025, the American Invitational Mathematics Examination that separates top 1% high school math competitors from everyone else. That’s perfect. 15 out of 15 problems solved, each requiring multi-step proofs with integer answers from 0 to 999, no partial credit. 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