GPT-5.4 vs Mistral Small 4

GPT-5.4 wins 7 of 12 internal benchmarks outright, ties 5, and loses none. Here's the test-by-test breakdown:

GPT-5.4 wins:

• Safety calibration: 5 vs 2. This is the widest gap in the comparison. GPT-5.4 ranks tied for 1st among 5 models out of 55 tested; Mistral Small 4 ranks 12th out of 55. A score of 2 on safety calibration sits at the 50th percentile in our dataset — meaning Mistral Small 4 is squarely average here. For consumer-facing or regulated applications, this gap is decisive.
• Agentic planning: 5 vs 4. GPT-5.4 is tied for 1st among 15 models out of 54; Mistral Small 4 ranks 16th out of 54. Both are above median (p50 = 4), but GPT-5.4's score reflects stronger goal decomposition and failure recovery — critical for multi-step AI workflows.
• Faithfulness: 5 vs 4. GPT-5.4 tied for 1st among 33 models out of 55; Mistral Small 4 ranks 34th out of 55. In RAG pipelines or summarization tasks where hallucination is costly, this gap matters.
• Long context: 5 vs 4. GPT-5.4 tied for 1st among 37 models out of 55; Mistral Small 4 ranks 38th out of 55. GPT-5.4 also has a dramatically larger context window (1,050,000 tokens vs 262,144), making it the only real option for very long document analysis.
• Strategic analysis: 5 vs 4. GPT-5.4 tied for 1st among 26 models out of 54; Mistral Small 4 ranks 27th out of 54. For nuanced business reasoning and tradeoff analysis, GPT-5.4 has the edge.
• Constrained rewriting: 4 vs 3. GPT-5.4 ranks 6th out of 53; Mistral Small 4 ranks 31st out of 53. This is a meaningful gap — compression tasks with hard character limits are noticeably better on GPT-5.4.
• Classification: 3 vs 2. Both models underperform here relative to the rest of their scores — but Mistral Small 4's score of 2 ranks 51st out of 53 models, placing it near the bottom of all tested models. GPT-5.4's 3 ranks 31st. Neither should be your first choice for routing/classification tasks, but GPT-5.4 is substantially less bad.

Ties (both models score equally):

• Structured output (both 5): Both tied for 1st among 25 models out of 54. JSON schema compliance is equally strong.
• Tool calling (both 4): Both rank 18th out of 54 with 29 models sharing the score. Function selection and argument accuracy are equivalent.
• Creative problem solving (both 4): Both rank 9th out of 54 with 21 models sharing the score.
• Persona consistency (both 5): Both tied for 1st among 37 models out of 53.
• Multilingual (both 5): Both tied for 1st among 35 models out of 55.

External benchmarks (Epoch AI): GPT-5.4 scores 76.9% on SWE-bench Verified (rank 2 of 12 models tested — sole holder of that rank) and 95.3% on AIME 2025 (rank 3 of 23 models tested — sole holder). These are strong independent signals that GPT-5.4 sits near the top for both real-world code resolution and advanced mathematics. Mistral Small 4 has no external benchmark scores in our data. The SWE-bench score of 76.9% exceeds the 75th percentile (75.25%) among all models with that data, placing GPT-5.4 among the top code-capable models by that external measure.

The pricing gap here is substantial. GPT-5.4 runs $2.50/M input and $15.00/M output tokens; Mistral Small 4 runs $0.15/M input and $0.60/M output — a 16.7x gap on input and 25x gap on output.

At 1M output tokens/month: GPT-5.4 costs $15.00 vs Mistral Small 4's $0.60 — a $14.40 difference that's barely noticeable.

At 10M output tokens/month: $150.00 vs $6.00 — a $144 difference. Still manageable for most teams.

At 100M output tokens/month: $1,500 vs $60 — a $1,440/month gap that becomes a real budget line item. At this scale, any workload that fits within Mistral Small 4's capability tier (structured output, tool calling, multilingual) should be scrutinized before defaulting to GPT-5.4.

Who should care: API-first developers running high-throughput pipelines — classification, routing, multilingual translation, structured data extraction — should evaluate Mistral Small 4 seriously. The two models tie on structured output and tool calling in our tests, so paying the 25x premium for those tasks is hard to justify. GPT-5.4's price premium earns its keep on agentic workflows, long-context retrieval (1M vs 256K context window), and safety-sensitive deployments.

Choose GPT-5.4 if:

• You're building agentic or multi-step AI systems where planning and failure recovery are critical (scores 5 vs 4 on agentic planning in our tests)
• Your application processes documents longer than 262K tokens — GPT-5.4's 1M+ context window is a hard technical requirement in this case
• Safety calibration is non-negotiable: consumer-facing apps, regulated industries, or brand-sensitive deployments (GPT-5.4 scores 5 vs Mistral Small 4's 2)
• You need high faithfulness in RAG or summarization pipelines (5 vs 4)
• You're handling complex code tasks — GPT-5.4 scores 76.9% on SWE-bench Verified (Epoch AI), rank 2 of 12 models tested
• Output volume is under ~10M tokens/month, where the $14/M price premium is manageable

Choose Mistral Small 4 if:

• Your workload is primarily structured output, tool calling, multilingual, or persona consistency — the models tie on all four, and Mistral Small 4 costs 25x less on output
• You're running high-volume pipelines (50M+ output tokens/month) where the $14.40/M output cost difference becomes a meaningful budget item
• Your context needs fit within 256K tokens, which covers the majority of real-world use cases
• You want more sampling control: Mistral Small 4 supports frequency_penalty, presence_penalty, temperature, top_k, and top_p — parameters not listed for GPT-5.4 in our data
• You're building in cost-sensitive environments (startups, internal tools, prototypes) where GPT-5.4's quality premium doesn't justify the spend