Llama 4 Maverick vs Mistral Small 4

Across our 12-test benchmark suite, Mistral Small 4 wins 6 tests outright, Llama 4 Maverick wins 1, and 5 are tied.

Where Mistral Small 4 wins:

• Structured output (5 vs 4): Mistral Small 4 ties for 1st of 54 models in JSON schema compliance and format adherence — a critical capability for developers building pipelines that parse model output. Llama 4 Maverick scores 4, ranking 26th of 54.
• Strategic analysis (4 vs 2): The widest gap in this comparison. Mistral Small 4 scores 4, ranking 27th of 54, while Llama 4 Maverick scores just 2, ranking 44th of 54. For tasks requiring nuanced tradeoff reasoning with real numbers — financial analysis, competitive assessments, scenario planning — this is a significant practical difference.
• Creative problem solving (4 vs 3): Mistral Small 4 ranks 9th of 54, producing non-obvious and feasible ideas at a meaningfully higher rate than Llama 4 Maverick at rank 30th of 54.
• Tool calling (4 vs not tested): Mistral Small 4 scores 4 on function selection, argument accuracy, and sequencing, ranking 18th of 54. Llama 4 Maverick's tool calling score is absent from our data — notably, the payload flags that Maverick's tool calling test hit a 429 rate limit on OpenRouter on 2026-04-13, likely a transient infrastructure issue rather than a capability failure. Developers should test Maverick's tool calling independently before assuming it matches Mistral Small 4.
• Agentic planning (4 vs 3): Mistral Small 4 ranks 16th of 54 on goal decomposition and failure recovery; Llama 4 Maverick ranks 42nd of 54. For agentic workflows where a model must plan multi-step tasks and recover from errors, this gap matters.
• Multilingual (5 vs 4): Mistral Small 4 ties for 1st of 55 models; Llama 4 Maverick scores 4, ranking 36th of 55. Both handle multilingual output, but Mistral Small 4 is more consistently equivalent-quality across non-English languages.

Where Llama 4 Maverick wins:

• Classification (3 vs 2): Llama 4 Maverick scores 3, ranking 31st of 53 on accurate categorization and routing. Mistral Small 4 scores 2, ranking 51st of 53 — near the bottom of the field. If your use case is primarily routing or categorization, this is Maverick's one clear advantage.

Tied benchmarks (5 tests):

• Persona consistency (5/5): Both models tie for 1st of 53 models — strong performance shared by 37 models total.
• Faithfulness (4/4): Both rank 34th of 55, tied with 18 models. Solid but not exceptional.
• Long context (4/4): Both rank 38th of 55 on retrieval accuracy at 30K+ tokens.
• Constrained rewriting (3/3): Both rank 31st of 53 — mid-field performance on compression within hard character limits.
• Safety calibration (2/2): Both rank 12th of 55, though a score of 2 sits at or below the 25th percentile (p25=1, p50=2). Neither model excels here relative to the broader field.

Both models are priced identically: $0.15 per million input tokens and $0.60 per million output tokens. At 1M output tokens/month, you spend $0.60 on either model. At 10M output tokens/month, that's $6.00. At 100M output tokens/month, $60.00 — again, the same for both. The pricing equation is a non-factor here. Your decision should rest entirely on capability fit, not cost. If you're running high-volume pipelines and were hoping to find a cheaper option among these two, you'll need to look elsewhere — these two are in lockstep on price.

Choose Mistral Small 4 if you're building agentic systems, API-integrated tools, or pipelines that require structured JSON output — it scores 4 vs Maverick's 3 on agentic planning, 5 vs 4 on structured output, and has a verified tool calling score of 4 where Maverick's result was rate-limited during testing. It also wins clearly on strategic analysis (4 vs 2) and creative problem solving (4 vs 3), making it the better fit for analytical writing, content generation, and reasoning-heavy tasks. Mistral Small 4 also supports an include_reasoning / reasoning parameter not present in Maverick's parameter list, which may be useful for transparency in decision pipelines. Additionally, if you need multilingual quality at scale, Mistral Small 4 ties for 1st of 55 models vs Maverick's rank 36.

Choose Llama 4 Maverick if classification and routing are your primary workload — it scores 3 vs Mistral Small 4's 2, with Mistral ranking near the bottom at 51st of 53 models on that test. Maverick also supports parameters like min_p, logit_bias, repetition_penalty, top_k, and tool_choice that Mistral Small 4 does not list, which may matter for fine-grained generation control. Its 1M-token context window (vs Mistral Small 4's 262K) is a meaningful advantage for very long document processing, though both score identically on our long context benchmark. At identical pricing, Maverick is the right call only for classification-heavy or very-long-context workflows.