Grok Code Fast 1 vs Mistral Large 3 2512

Across our 12-test benchmark suite, Grok Code Fast 1 and Mistral Large 3 2512 split the wins evenly at 4 each, with 4 ties — so neither model dominates overall.

Where Grok Code Fast 1 wins:

• Agentic planning: 5/5 vs 4/5. Grok Code Fast 1 ties for 1st of 54 models; Mistral Large 3 2512 ranks 16th of 54. This is the biggest functional gap between them. Agentic planning tests goal decomposition and failure recovery — the core skills for autonomous coding agents and multi-step tool use. The reasoning trace support in Grok Code Fast 1 likely contributes here.
• Classification: 4/5 vs 3/5. Grok Code Fast 1 ties for 1st of 53 models; Mistral Large 3 2512 ranks 31st of 53. For routing, tagging, or intent detection pipelines, Grok Code Fast 1 is meaningfully stronger.
• Safety calibration: 2/5 vs 1/5. Neither model performs well here — both score below the field median of 2 — but Grok Code Fast 1 (ranked 12th of 55) outperforms Mistral Large 3 2512 (ranked 32nd of 55). This matters for applications serving general consumers where over-refusal or under-refusal are both costly.
• Persona consistency: 4/5 vs 3/5. Grok Code Fast 1 ranks 38th of 53 at this score; Mistral Large 3 2512 ranks 45th of 53. Both are in the lower half of the field, but Grok Code Fast 1 holds character more reliably under injection attempts.

Where Mistral Large 3 2512 wins:

• Structured output: 5/5 vs 4/5. Mistral Large 3 2512 ties for 1st of 54 models on JSON schema compliance and format adherence. Grok Code Fast 1 ranks 26th of 54 at 4/5. For API integrations, data extraction pipelines, or any workflow requiring strict schema conformance, this is a meaningful advantage.
• Faithfulness: 5/5 vs 4/5. Mistral Large 3 2512 ties for 1st of 55; Grok Code Fast 1 ranks 34th of 55. Mistral Large 3 2512 is significantly better at staying grounded in source material without hallucinating — critical for summarization, RAG, and document Q&A.
• Strategic analysis: 4/5 vs 3/5. Mistral Large 3 2512 ranks 27th of 54; Grok Code Fast 1 ranks 36th of 54. For nuanced tradeoff reasoning over real numbers — financial analysis, competitive assessments, policy evaluation — Mistral Large 3 2512 produces sharper outputs.
• Multilingual: 5/5 vs 4/5. Mistral Large 3 2512 ties for 1st of 55 models; Grok Code Fast 1 ranks 36th of 55. For non-English applications, the gap is substantial in practical terms.

Ties (both score equally):

• Tool calling: both 4/5, both rank 18th of 54 — identical performance on function selection and argument accuracy.
• Long context: both 4/5, both rank 38th of 55 — comparable retrieval at 30K+ tokens.
• Constrained rewriting: both 3/5, both rank 31st of 53.
• Creative problem solving: both 3/5, both rank 30th of 54.

Neither model has external benchmark scores (SWE-bench Verified, AIME 2025, MATH Level 5) in the payload, so we cannot supplement internal scores with third-party coding or math data for this comparison.

The output cost is identical for both models at $1.50 per million tokens. The only pricing difference is on input: Grok Code Fast 1 charges $0.20/M input tokens versus Mistral Large 3 2512's $0.50/M — a 2.5× gap on the input side. At 1M input tokens/month, that's a $0.30 difference — trivial. At 10M input tokens/month, Grok Code Fast 1 saves $3.00 on input costs. At 100M input tokens/month, the input savings grow to $30 — still modest relative to most API budgets. In practice, for output-heavy workloads (long generations, agentic loops), the two models cost essentially the same. The input cost advantage of Grok Code Fast 1 becomes meaningful only for pipelines that feed very large documents or long conversation histories into the model at high volume, such as document analysis or RAG over large corpora. If you're running a reasoning-heavy agentic system where Grok Code Fast 1's reasoning tokens add to input length, model the actual token breakdown before assuming savings.

Choose Grok Code Fast 1 if: You are building agentic coding systems, autonomous pipelines, or any workflow where multi-step planning and failure recovery are central — its 5/5 agentic planning score (tied for 1st of 54 models) is a genuine differentiator. It's also the better pick for classification-heavy routing pipelines and for applications where slightly better safety calibration matters. The visible reasoning traces are useful for debugging and steering agent behavior. The lower input cost ($0.20 vs $0.50/M tokens) adds marginal savings at high input volumes.

Choose Mistral Large 3 2512 if: Your application depends on strict JSON schema compliance (5/5, tied for 1st of 54), grounded summarization or RAG where hallucination is a real risk (5/5 faithfulness, tied for 1st of 55), multilingual output quality (5/5, tied for 1st of 55), or strategic analysis tasks requiring nuanced reasoning. Mistral Large 3 2512 also accepts image inputs, which Grok Code Fast 1 does not — a hard requirement for any multimodal workflow. For content pipelines, document-grounded Q&A, and international deployments, Mistral Large 3 2512 is the stronger choice.