GPT-5.4 vs Mistral Small 3.1 24B

Across our 12-test internal benchmark suite, GPT-5.4 wins 10 categories outright, ties 2 (classification and long context), and loses none.

Where GPT-5.4 dominates:

• Agentic Planning (5 vs 3): GPT-5.4 ties for 1st among 54 models; Mistral Small 3.1 24B ranks 42nd. For goal decomposition and multi-step task recovery, this is a significant functional difference.
• Tool Calling (4 vs 1): GPT-5.4 ranks 18th of 54. Mistral Small 3.1 24B ranks 53rd of 54 — and the payload confirms a no_tool calling quirk, meaning this score reflects a near-total absence of this capability. Any workflow requiring function calls or API orchestration is a non-starter on Mistral Small 3.1 24B.
• Safety Calibration (5 vs 1): GPT-5.4 ranks in the top 5 of 55 models; Mistral Small 3.1 24B ranks 32nd. At a score of 1, Mistral Small 3.1 24B sits at the bottom quartile (p25 = 1 across all 52 models). For production apps with sensitive content requirements, this gap is material.
• Strategic Analysis (5 vs 3): GPT-5.4 ties for 1st of 54 models; Mistral Small 3.1 24B ranks 36th. On nuanced tradeoff reasoning with real numbers, the gap is two full points.
• Creative Problem Solving (4 vs 2): GPT-5.4 ranks 9th of 54; Mistral Small 3.1 24B ranks 47th. For generating non-obvious, feasible ideas, Mistral Small 3.1 24B falls well below the median (p50 = 4).
• Persona Consistency (5 vs 2): GPT-5.4 ties for 1st of 53; Mistral Small 3.1 24B ranks 51st of 53 — near the bottom.
• Faithfulness (5 vs 4): GPT-5.4 ties for 1st of 55; Mistral Small 3.1 24B ranks 34th. Both are above median, but GPT-5.4 has a clear edge for RAG and summarization tasks.
• Structured Output (5 vs 4): GPT-5.4 ties for 1st of 54; Mistral Small 3.1 24B ranks 26th. Both score above median (p50 = 4), but GPT-5.4's supported_parameters include structured outputs explicitly, while Mistral Small 3.1 24B does not list this parameter.
• Constrained Rewriting (4 vs 3): GPT-5.4 ranks 6th of 53; Mistral Small 3.1 24B ranks 31st. One point gap, but GPT-5.4 is clearly above median (p50 = 4); Mistral Small 3.1 24B is at the 25th percentile.
• Multilingual (5 vs 4): GPT-5.4 ties for 1st of 55; Mistral Small 3.1 24B ranks 36th. Both are functional, but GPT-5.4 delivers more consistent quality across non-English languages.

Where they tie:

• Long Context (5 vs 5): Both tie for 1st among 55 models. However, GPT-5.4 has a 1,050,000-token context window vs Mistral Small 3.1 24B's 128,000 tokens — so while both score maximum on our 30K+ retrieval test, GPT-5.4's absolute capacity is dramatically larger.
• Classification (3 vs 3): Both rank 31st of 53. Neither model shines here; this is a weak point for GPT-5.4 relative to its elsewhere-strong performance.

External benchmarks (Epoch AI): GPT-5.4 scores 76.9% on SWE-bench Verified (rank 2 of 12 models tested), placing it among the top coding models by that external measure. On AIME 2025, it scores 95.3% (rank 3 of 23 models tested) — well above the median of 83.9%. No external benchmark scores are available for Mistral Small 3.1 24B in our data, so direct external comparison is not possible.

GPT-5.4 costs $2.50/M input tokens and $15.00/M output tokens. Mistral Small 3.1 24B costs $0.35/M input and $0.56/M output — a 7.1x input gap and a 26.8x output gap. In practice: at 1M output tokens/month, GPT-5.4 costs $15 vs $0.56 — a difference of $14.44. At 10M output tokens/month, that gap grows to $144.40. At 100M output tokens/month, you're looking at $1,500 for GPT-5.4 vs $56 for Mistral Small 3.1 24B — a $1,444 monthly difference. For consumer apps, internal tooling, or batch processing at scale, that cost gap is decisive. Developers building agentic pipelines or applications requiring tool calling, however, should note that Mistral Small 3.1 24B has a confirmed no_tool calling quirk in our data — meaning the entire tool-calling use case is eliminated regardless of price.

Choose GPT-5.4 if:

• You need agentic or multi-step pipelines — its score of 5 vs 3 on agentic planning and functional tool calling (vs Mistral Small 3.1 24B's confirmed no_tool calling limitation) make it the only viable option for these workflows.
• Safety calibration matters for your deployment — GPT-5.4 scores 5 vs 1, placing in the top 5 of 55 models while Mistral Small 3.1 24B sits in the bottom half.
• You're building coding assistants or AI-driven development tools — 76.9% on SWE-bench Verified (Epoch AI, rank 2 of 12) and 95.3% on AIME 2025 (rank 3 of 23) support this use case with hard data.
• Your context needs exceed 128K tokens — GPT-5.4's 1M+ token window is not matched by Mistral Small 3.1 24B.
• Persona consistency or character fidelity matters — a score of 5 vs 2 (rank 1 vs rank 51 of 53) is a decisive gap.

Choose Mistral Small 3.1 24B if:

• You're running high-volume, output-heavy workloads where the $0.56/M vs $15/M output cost gap is the primary constraint — at 100M output tokens/month, you save over $1,400.
• Your tasks are limited to straightforward text reasoning, summarization, or multilingual processing where long-context retrieval (5/5) and faithfulness (4/5) are sufficient.
• You do not require tool calling, structured outputs parameter support, or agentic planning.
• Budget is fixed and you cannot justify frontier-model pricing for the tasks at hand.