GPT-5.4 vs Mistral Large 3 2512

Across our 12-test internal benchmark suite (scored 1–5), GPT-5.4 leads on 7 tests and ties on 5. Mistral Large 3 2512 wins none.

Where GPT-5.4 leads:

• Safety calibration: GPT-5.4 scores 5/5 vs Mistral Large 3 2512's 1/5 — the single sharpest gap in the dataset. GPT-5.4 ties for 1st among 5 models out of 55 tested; Mistral Large 3 2512 ranks 32nd of 55. A score of 1 sits at the 25th percentile of all models we test — meaning this is a genuine weakness, not a minor miss. For any deployment that involves user-facing applications or content policy enforcement, this is a disqualifying difference.

• Persona consistency: GPT-5.4 scores 5/5 (tied 1st of 53) vs Mistral Large 3 2512's 3/5 (rank 45 of 53). Mistral Large 3 2512 falls in the bottom 20% on this test. For chatbots, role-based assistants, or any system requiring stable character, this matters.

• Long context: GPT-5.4 scores 5/5 (tied 1st of 55) vs Mistral Large 3 2512's 4/5 (rank 38 of 55). Both are above median (p50 = 5), but GPT-5.4's context window of 1,050,000 tokens dwarfs Mistral Large 3 2512's 262,144 — and the benchmark score reflects better retrieval accuracy at depth. For document processing or multi-session context, GPT-5.4 has a structural advantage.

• Agentic planning: GPT-5.4 scores 5/5 (tied 1st of 54) vs Mistral Large 3 2512's 4/5 (rank 16 of 54). Both are above median, but GPT-5.4's top-tier goal decomposition and failure recovery make it more reliable in automated agent loops.

• Strategic analysis: GPT-5.4 scores 5/5 (tied 1st of 54) vs Mistral Large 3 2512's 4/5 (rank 27 of 54). For nuanced tradeoff reasoning with real numbers — financial modeling, competitive analysis, decision support — GPT-5.4 is materially stronger.

• Creative problem solving: GPT-5.4 scores 4/5 (rank 9 of 54) vs Mistral Large 3 2512's 3/5 (rank 30 of 54). Mistral Large 3 2512 sits at the 25th percentile here; GPT-5.4 is in the top quarter.

• Constrained rewriting: GPT-5.4 scores 4/5 (rank 6 of 53) vs Mistral Large 3 2512's 3/5 (rank 31 of 53). For compression within hard character limits — ad copy, UI strings, summaries — GPT-5.4 is more reliable.

Where they tie:

• Structured output (both 5/5, tied 1st of 54): Both models deliver equivalent JSON schema compliance.
• Tool calling (both 4/5, both rank 18 of 54): Equivalent function selection and argument accuracy.
• Faithfulness (both 5/5, tied 1st of 55): Both stick to source material without hallucinating.
• Classification (both 3/5, both rank 31 of 53): Both sit at median or below — neither excels here.
• Multilingual (both 5/5, tied 1st of 55): Equivalent quality in non-English languages.

External benchmarks (Epoch AI):

GPT-5.4 scores 76.9% on SWE-bench Verified (rank 2 of 12 models tested — sole holder of that score), placing it above the 75th percentile cutoff of 75.25% among models with scores. On AIME 2025, GPT-5.4 scores 95.3% (rank 3 of 23 models tested), well above the median of 83.9%. Mistral Large 3 2512 has no external benchmark scores in our data. These third-party results reinforce GPT-5.4's strength in coding and advanced math — relevant for developer tooling and STEM applications.

GPT-5.4 is priced at $2.50/M input tokens and $15.00/M output tokens. Mistral Large 3 2512 runs $0.50/M input and $1.50/M output — exactly one-fifth the input cost and one-tenth the output cost.

At 1M output tokens/month, GPT-5.4 costs $15.00 vs $1.50 for Mistral Large 3 2512 — a $13.50 difference that's easy to absorb. At 10M output tokens/month, that gap reaches $135. At 100M output tokens/month — a realistic scale for high-volume production APIs — you're paying $1,500 vs $150, a $1,350/month difference.

Who should care: hobbyists and low-volume developers can default to GPT-5.4 if they want the stronger performer without budget pressure. Teams running millions of inferences daily need to quantify exactly which benchmarks matter to their use case. If your workload is dominated by structured output, tool calling, faithfulness, multilingual, or classification — all tied between the two models in our testing — Mistral Large 3 2512 delivers equivalent results at a fraction of the cost. If your workload depends on agentic pipelines, long-document retrieval, safety-sensitive deployments, or complex strategic reasoning, the GPT-5.4 performance advantage may justify the 10x price.

Choose GPT-5.4 if:

• Your application involves safety-sensitive outputs, user-facing content, or content policy enforcement — the 5/5 vs 1/5 safety calibration gap is too large to ignore.
• You're building autonomous agents or multi-step pipelines where agentic planning (5/5, tied 1st of 54) and failure recovery matter.
• You need to process very long documents — GPT-5.4's 1M+ token context window and top-ranked long context score give it a structural edge.
• Your work involves strategic reasoning, competitive analysis, or decision support where the 5/5 vs 4/5 strategic analysis gap translates to meaningfully better outputs.
• You're doing serious coding or math work — GPT-5.4 scores 76.9% on SWE-bench Verified and 95.3% on AIME 2025 (Epoch AI), both placing it among the top models tested.
• Budget is not the primary constraint and you want the best-performing model across the broadest set of tasks.

Choose Mistral Large 3 2512 if:

• Your primary tasks are structured output, tool calling, faithfulness, classification, or multilingual — all tied with GPT-5.4 in our testing — and you want to capture the 10x output cost saving.
• You're running high-volume production workloads where the $13.50/M output token gap compounds into thousands of dollars per month.
• You don't need persona consistency or safety calibration at the highest tier — Mistral Large 3 2512's weaknesses there are real but may be irrelevant to your application.
• You want an Apache 2.0-licensed architecture (675B total parameters, 41B active) with a capable but cost-efficient API profile.