Devstral Small 1.1 vs Grok 4.20

Across our 12-test suite, Grok 4.20 wins 10 benchmarks, ties 1 (classification), and loses 1 (safety calibration). Here is the test-by-test breakdown:

Tool Calling (5 vs 4): Grok 4.20 scores 5/5, tied for 1st among 17 models out of 54 tested. Devstral Small 1.1 scores 4/5, tied for 18th among 29 models. For agentic workflows that depend on accurate function selection and argument sequencing, this is a meaningful edge — Grok 4.20 sits at the top of the distribution while Devstral Small 1.1 lands in the middle third.

Agentic Planning (4 vs 2): This is Devstral Small 1.1's sharpest weakness — it scores 2/5 and ranks 53rd of 54 models in our testing. Grok 4.20 scores 4/5 (rank 16 of 54). Given that Devstral Small 1.1 is explicitly designed for software engineering agents, this score warrants caution for autonomous, multi-step task execution.

Strategic Analysis (5 vs 2): Grok 4.20 scores 5/5, tied for 1st among 26 models out of 54. Devstral Small 1.1 scores 2/5, ranking 44th of 54. This covers nuanced tradeoff reasoning with real numbers — a significant gap for analytical or decision-support use cases.

Creative Problem Solving (4 vs 2): Grok 4.20 scores 4/5 (rank 9 of 54); Devstral Small 1.1 scores 2/5 (rank 47 of 54). This measures non-obvious, feasible idea generation. Devstral Small 1.1 is near the bottom of tested models.

Persona Consistency (5 vs 2): Grok 4.20 scores 5/5, tied for 1st among 37 models out of 53. Devstral Small 1.1 scores 2/5, ranking 51st of 53. For chatbot or assistant applications requiring stable character maintenance, this is a disqualifying gap.

Faithfulness (5 vs 4): Grok 4.20 scores 5/5, tied for 1st among 33 models out of 55. Devstral Small 1.1 scores 4/5 (rank 34 of 55). Both are solid on sticking to source material, but Grok 4.20 sits at the ceiling.

Structured Output (5 vs 4): Grok 4.20 scores 5/5, tied for 1st among 25 models. Devstral Small 1.1 scores 4/5, tied for 26th. Both handle JSON schema compliance reliably; Grok 4.20 is marginally better.

Long Context (5 vs 4): Grok 4.20 scores 5/5 (tied for 1st, 37 models) and offers a 2,000,000-token context window. Devstral Small 1.1 scores 4/5 with a 131,072-token context window. The context window difference is massive — Grok 4.20 can process documents or codebases that Devstral Small 1.1 cannot fit at all.

Constrained Rewriting (4 vs 3): Grok 4.20 scores 4/5 (rank 6 of 53); Devstral Small 1.1 scores 3/5 (rank 31 of 53). Compression within hard character limits favors Grok 4.20.

Multilingual (5 vs 4): Grok 4.20 scores 5/5, tied for 1st among 35 models. Devstral Small 1.1 scores 4/5 (rank 36 of 55). Grok 4.20 is better for non-English deployments.

Classification (4 vs 4 — tie): Both models score 4/5 and are both tied for 1st of 53 models in this category. Accurate routing and categorization is equivalent between them.

Safety Calibration (2 vs 1): Devstral Small 1.1's only outright win. It scores 2/5 (rank 12 of 55, 20 models share this score). Grok 4.20 scores 1/5 (rank 32 of 55). Neither model performs strongly here — both are below the median (p50 = 2) — but Devstral Small 1.1 is the better option if calibrated refusal behavior matters. Note that Grok 4.20's score of 1/5 places it in the bottom quartile of tested models on this dimension.

Devstral Small 1.1 costs $0.10/M input tokens and $0.30/M output tokens. Grok 4.20 costs $2.00/M input and $6.00/M output — 20x more expensive on input and 20x more on output.

At 1M output tokens/month: Devstral Small 1.1 costs $0.30 vs Grok 4.20's $6.00 — a $5.70 difference, largely irrelevant at this scale.

At 10M output tokens/month: $3 vs $60 — a $57 monthly gap that starts to matter for early-stage products.

At 100M output tokens/month: $300 vs $6,000 — a $5,700/month difference that is a significant budget line for any production system. Teams processing hundreds of millions of tokens (batch document processing, high-volume code generation, multi-turn chat at scale) will feel this gap acutely.

Devstral Small 1.1 also supports a broader parameter set including frequency_penalty, presence_penalty, and seed — useful for fine-grained generation control. Grok 4.20 adds include_reasoning, reasoning, logprobs, and top_logprobs parameters, which are relevant for developers who need to inspect model reasoning chains or token probabilities. Grok 4.20 also accepts image and file inputs (text+image+file->text), while Devstral Small 1.1 is text-only. If your pipeline requires multimodal input, Devstral Small 1.1 cannot substitute regardless of cost.

Choose Devstral Small 1.1 if: you are running high-volume, cost-sensitive workloads (100M+ tokens/month) where the $5,700/month savings justifies lower scores on planning and reasoning; your pipeline is text-only; you prioritize safety calibration; or you are experimenting with a narrow, structured task (classification, JSON output) where both models are competitive and cost matters more than marginal quality gains.

Choose Grok 4.20 if: you are building agentic or autonomous systems where agentic planning (4 vs 2) and tool calling (5 vs 4) directly determine reliability; your application requires multimodal input (images, files) since Devstral Small 1.1 is text-only; you need to process documents or codebases exceeding 131K tokens, where Grok 4.20's 2M-token context window is the only viable option; or your use case involves strategic analysis, creative problem solving, or persona-consistent assistants where Grok 4.20's score advantages (5 vs 2 on strategic analysis, 5 vs 2 on persona consistency) translate directly to output quality.