Devstral Small 1.1 vs Grok 3

In our 12-test suite, Grok 3 wins 8 categories, Devstral Small 1.1 wins none, and 4 are ties. Specifics (our scores):

• Structured output: Grok 3 = 5 vs Devstral = 4. Grok is tied for 1st on structured output (tied with 24 others out of 54) while Devstral sits at rank 26/54. This means Grok is more reliable for strict JSON/schema compliance in production integrations.
• Strategic analysis: Grok 3 = 5 vs Devstral = 2. Grok ranks tied for 1st (1/54), Devstral ranks 44/54 — real-world implication: Grok handles nuanced tradeoffs and numeric reasoning for decision support far better in our tests.
• Creative problem solving: Grok 3 = 3 vs Devstral = 2. Grok outperforms Devstral on non-obvious, feasible idea generation (rank 30 vs Devstral rank 47).
• Faithfulness: Grok 3 = 5 vs Devstral = 4. Grok is tied for 1st on faithfulness (1/55) while Devstral ranks 34/55 — Grok is less likely to hallucinate on source-constrained tasks in our testing.
• Long context: Grok 3 = 5 vs Devstral = 4. Grok ties for 1st on retrieval accuracy at 30K+ tokens (1/55); Devstral ranks 38/55 — choose Grok for large-document workflows.
• Persona consistency: Grok 3 = 5 vs Devstral = 2. Grok is tied for 1st (1/53); Devstral is near the bottom (rank 51/53) — important for bots that must maintain character and resist injection.
• Agentic planning: Grok 3 = 5 vs Devstral = 2. Grok ties for 1st (1/54); Devstral ranks 53/54 — Grok is clearly stronger for goal decomposition and multi-step recovery.
• Multilingual: Grok 3 = 5 vs Devstral = 4. Grok tied for 1st (1/55); Devstral ranks 36/55 — Grok offers better parity across languages in our tests. Ties (no clear winner in our testing): constrained_rewriting (3/3), tool_calling (4/4), classification (4/4), safety_calibration (2/2). Notably, both models score 4 on tool calling and tie for 1st in classification, so for function selection and routing they are comparable. Context window is identical (131,072) per the payload. Overall, Grok 3’s consistent top ranks across strategic analysis, faithfulness, long-context, agentic planning, and persona consistency explain its majority wins; Devstral holds baseline competence on practical integration tasks at a fraction of the cost.

Per the payload, Devstral Small 1.1 costs $0.10 per mTok input and $0.30 per mTok output; Grok 3 costs $3.00 per mTok input and $15.00 per mTok output. Assuming a 50/50 split of input vs output tokens, 1M combined tokens (500k input + 500k output = 500 mTok each) costs: Devstral ≈ $200 (0.1500 + 0.3500) and Grok ≈ $9,000 (3500 + 15500). Scale-up: 10M combined tokens → Devstral ≈ $2,000; Grok ≈ $90,000. 100M combined tokens → Devstral ≈ $20,000; Grok ≈ $900,000. The cost gap grows linearly: teams optimizing unit economics at 10M+ tokens/month will notice six-figure differences; startups, hobbyists, and high-volume data pipelines should care most about Devstral’s 30–50x lower per-side prices ($0.1 vs $3 input; $0.3 vs $15 output).

Choose Devstral Small 1.1 if: you need a low-cost model for high-volume classification, routine tool-calling, or production agents where unit cost dominates (costs ≈ $200 per 1M combined tokens under a 50/50 split). Choose Grok 3 if: you need top-tier long-context retrieval, strict structured outputs, strong faithfulness, multilingual parity, agentic planning, or persona consistency for enterprise apps and can absorb much higher runtime costs (~$9,000 per 1M combined tokens under the same split). If you’re unsure, use Devstral for early-stage, cost-constrained development and switch to Grok 3 for mission-critical pipelines that require the higher-ranking capabilities shown in our tests.