Devstral 2 2512 vs Grok 4.20

Across our 12-test suite, Grok 4.20 wins 5 benchmarks outright, Devstral 2 2512 wins 1, and they tie on 6.

Where Grok 4.20 leads:

• Tool calling (5 vs 4): Grok 4.20 ties for 1st among 54 models (with 16 others), while Devstral 2 2512 ranks 18th (tied with 28 others). For agentic workflows — function selection, argument accuracy, sequencing — this gap matters directly.
• Faithfulness (5 vs 4): Grok 4.20 ties for 1st among 55 models (32 others share this); Devstral 2 2512 ranks 34th. In RAG pipelines or document-grounded tasks, Grok 4.20's higher score means fewer hallucinations against source material.
• Strategic analysis (5 vs 4): Grok 4.20 ties for 1st of 54 (25 others); Devstral 2 2512 ranks 27th. For nuanced tradeoff reasoning with real numbers, Grok 4.20 is in the top tier.
• Classification (4 vs 3): Grok 4.20 ties for 1st of 53 (29 others); Devstral 2 2512 ranks 31st. Routing and categorization tasks favor Grok 4.20 meaningfully — Devstral 2 2512's score of 3 sits below the field median of 4.
• Persona consistency (5 vs 4): Grok 4.20 ties for 1st of 53 (36 others); Devstral 2 2512 ranks 38th. For chatbot or role-based deployments, Grok 4.20 holds character more reliably.

Where Devstral 2 2512 leads:

• Constrained rewriting (5 vs 4): Devstral 2 2512 ties for 1st of 53 (4 other models — a much smaller group than Grok 4.20's ties elsewhere); Grok 4.20 ranks 6th. For compression tasks with hard character limits, Devstral 2 2512 has a genuine edge.

Where they tie (6 benchmarks):

• Structured output (both 5/5, tied for 1st of 54)
• Long context (both 5/5, tied for 1st of 55)
• Creative problem solving (both 4/5, tied rank 9 of 54)
• Safety calibration (both 1/5, tied rank 32 of 55 — both score below the field median of 2, which warrants attention for sensitive deployments)
• Agentic planning (both 4/5, tied rank 16 of 54)
• Multilingual (both 5/5, tied for 1st of 55)

The safety calibration tie at 1/5 is notable — both models score well below the field median in our testing. Neither should be deployed in contexts requiring robust refusal of harmful requests without additional safeguards.

Devstral 2 2512's 256K context window and Grok 4.20's 2M context window both deliver the same long-context score of 5/5 in our testing, though Grok 4.20's 2M window provides headroom for workloads that exceed 256K tokens.

Devstral 2 2512 costs $0.40/M input tokens and $2.00/M output tokens. Grok 4.20 costs $2.00/M input and $6.00/M output — 5x more expensive on input and 3x more on output. In practice, output cost dominates most workloads. At 1M output tokens/month, Devstral 2 2512 runs you $2 vs Grok 4.20's $6 — a $4 difference that's negligible. At 10M output tokens/month, that gap grows to $40 vs $60 — still manageable. At 100M output tokens/month, you're looking at $200 vs $600 — a $400/month delta that becomes a real line item for high-volume production systems. Developers running agentic pipelines or document processing at scale should weigh whether Grok 4.20's performance edge on faithfulness (5 vs 4) and tool calling (5 vs 4) justifies that cost multiple. For lower-volume or budget-sensitive deployments, Devstral 2 2512's $2/M output pricing makes it compelling, especially given it ties Grok 4.20 on 6 of 12 benchmarks.

Choose Devstral 2 2512 if: your primary use case is constrained rewriting (it ties for 1st of 53 models), you need solid structured output and long-context performance at $2/M output tokens, or you're running high-volume workloads where the 3x output cost premium for Grok 4.20 isn't justified by your task mix. It's also a reasonable choice if your tasks skew toward the 6 benchmarks where both models tie.

Choose Grok 4.20 if: you're building agentic pipelines (tool calling 5/5, tied for 1st), need high faithfulness in document-grounded or RAG applications (5/5 vs 4/5), require strong classification and routing (4/5 vs 3/5), or need persona consistency for conversational AI. The 2M context window also gives Grok 4.20 an architectural advantage for very large document workloads, even though both score identically in our 30K+ token long-context test. Pay the $6/M output premium when the quality delta on these tasks has real downstream value — at moderate volumes (under 10M output tokens/month), the absolute dollar difference is small.