Devstral Medium vs Grok 4.1 Fast

Summary: In our 12-test suite Grok 4.1 Fast wins 9 tests, Devstral Medium wins none, and 3 tests tie. Detailed walk-through (scores shown as Devstral → Grok):

• structured_output: 4 → 5 — Grok wins. In our testing Grok ties for 1st (tied with 24 others) while Devstral ranks 26 of 54; this matters for JSON schema compliance and strict format adherence in production APIs.
• strategic_analysis: 2 → 5 — Grok wins decisively (Grok tied for 1st of 54; Devstral ranks 44). For nuanced tradeoff reasoning with numbers, Grok is far stronger in our benchmarks.
• constrained_rewriting: 3 → 4 — Grok wins (rank 6 of 53 vs Devstral rank 31). If you compress content into hard character limits, Grok produced tighter, more accurate rewrites in our tests.
• creative_problem_solving: 2 → 4 — Grok wins (Grok rank 9 vs Devstral rank 47). For non-obvious, feasible ideas Grok scored higher on our creative tasks.
• tool_calling: 3 → 4 — Grok wins (rank 18 vs Devstral rank 47). Grok performed better at function selection, argument accuracy, and sequencing in our tool-calling scenarios.
• faithfulness: 4 → 5 — Grok wins (tied for 1st vs Devstral rank 34). Grok sticks to source material more reliably in our tests, reducing hallucination risk.
• long_context: 4 → 5 — Grok wins (tied for 1st of 55 vs Devstral rank 38). For retrieval and multi-file context at 30K+ tokens, Grok is measurably stronger.
• persona_consistency: 3 → 5 — Grok wins (tied for 1st vs Devstral rank 45). Grok maintained character and resisted injection better in our scenarios.
• multilingual: 4 → 5 — Grok wins (tied for 1st vs Devstral rank 36). Grok produced higher-quality non-English outputs in our tests.

Ties:

• classification: 4 → 4 — tie (both tied for 1st among many models). For routing/categorization both models perform similarly in our suite.
• safety_calibration: 1 → 1 — tie. Both models scored low on safety calibration in our tests and ranked similarly.
• agentic_planning: 4 → 4 — tie (both rank 16 of 54). For goal decomposition and failure recovery they performed comparably in our scenarios.

What this means for real tasks: Grok’s higher scores and top ranks on structured_output, long_context, tool_calling, faithfulness, and strategic_analysis make it the safer pick for production agentic workflows, multi-file code/context retrieval, and any use that requires strict output formats. Devstral matches Grok on classification and agentic planning only, but otherwise falls behind in our measured dimensions.

Devstral Medium input/output: $0.40/$2.00 per 1k tokens. Grok 4.1 Fast input/output: $0.20/$0.50 per 1k tokens. Assuming a 50/50 split of input vs output tokens: • 1M tokens (1,000 mTok) → Devstral ≈ $1,200 (500 mTok input * $0.40 = $200; 500 mTok output * $2.00 = $1,000), Grok ≈ $350 (500 mTok * $0.20 = $100; 500 mTok * $0.50 = $250). • 10M tokens → Devstral ≈ $12,000; Grok ≈ $3,500. • 100M tokens → Devstral ≈ $120,000; Grok ≈ $35,000. The priceRatio in the payload is 4x: Devstral’s output cost dominates high-volume bills. Teams shipping high-volume SaaS, analytics, or heavy-response apps should care; Grok materially reduces monthly cloud costs at scale.

Choose Grok 4.1 Fast if you need: long-context retrieval (2,000,000 token window), robust structured outputs (5 vs 4), better tool calling (4 vs 3), higher faithfulness and multilingual quality — and lower per-token costs ($0.20/$0.50 per 1k). Choose Devstral Medium if: your requirement is limited to classification or agentic planning parity (ties on those tests) and you have a specific reason to accept higher costs — otherwise Grok delivers more capability per dollar in our testing.