Devstral Small 1.1 vs GPT-5.4 Nano
GPT-5.4 Nano is the better pick for most production use cases that need long-context, strategic reasoning, multimodal inputs and persona consistency — it wins 9 of 12 benchmarks in our tests. Devstral Small 1.1 is the cost-efficient alternative and still wins classification (4 vs 3) if your workload is price-sensitive or classification-heavy.
mistral
Devstral Small 1.1
Benchmark Scores
External Benchmarks
Pricing
Input
$0.100/MTok
Output
$0.300/MTok
modelpicker.net
openai
GPT-5.4 Nano
Benchmark Scores
External Benchmarks
Pricing
Input
$0.200/MTok
Output
$1.25/MTok
modelpicker.net
Benchmark Analysis
Head-to-head by test (our 12-test suite): Devstral Small 1.1 wins classification (score 4 vs 3) and is tied on tool_calling (4 vs 4) and faithfulness (4 vs 4). GPT-5.4 Nano wins structured_output (5 vs 4; ranks tied for 1st on structured_output), strategic_analysis (5 vs 2; tied for 1st on strategic_analysis), constrained_rewriting (4 vs 3; ranks 6th of 53), creative_problem_solving (4 vs 2; ranks 9th of 54), long_context (5 vs 4; tied for 1st on long_context), safety_calibration (3 vs 2), persona_consistency (5 vs 2; tied for 1st on persona_consistency), agentic_planning (4 vs 2), and multilingual (5 vs 4; tied for 1st on multilingual). Rankings context: GPT-5.4 Nano sits at or near top for long-context, persona, structured outputs, and strategic analysis (multiple "tied for 1st" entries in our ranking table), while Devstral's classification score ties for 1st among 53 models. External benchmark note: GPT-5.4 Nano scores 87.8% on AIME 2025 (Epoch AI), ranking 8th of 23 on that contest. Practically, GPT-5.4 Nano will produce more reliable schema-compliant outputs, handle 30K+ token retrieval tasks better, maintain persona and plan agentic workflows more robustly; Devstral is a strong, lower-cost classifier and matches GPT-5.4 Nano on faithfulness and tool_calling in our tests.
Pricing Analysis
Devstral Small 1.1: input $0.10/1k, output $0.30/1k. GPT-5.4 Nano: input $0.20/1k, output $1.25/1k. Example costs (input+output equal volume): 1M tokens → Devstral $400 vs GPT-5.4 Nano $1,450; 10M → Devstral $4,000 vs GPT-5.4 Nano $14,500; 100M → Devstral $40,000 vs GPT-5.4 Nano $145,000. At high volumes (10M+ tokens/mo) the difference becomes material — GPT-5.4 Nano costs ~3.6x more on a balanced input+output basis. Teams with tight budgets or high throughput should prioritize Devstral; teams that need the higher-scoring capabilities listed below should budget for GPT-5.4 Nano.
Real-World Cost Comparison
Bottom Line
Choose Devstral Small 1.1 if you: need a lower-cost model for high-throughput or classification-centered workloads, want a text-only model with a 131,072-token context window and per-token costs of $0.10/$0.30 (input/output), or must minimize monthly inference bill (saves ~72–75% on per-token spend vs GPT-5.4 Nano). Choose GPT-5.4 Nano if you: require top-tier long-context retrieval, strategic numerical reasoning, consistent persona and structured JSON outputs (scores 5 vs 2–4 across those benchmarks), or need multimodal inputs (text+image+file) and can absorb higher costs ($0.20/$1.25 per 1k tokens).
How We Test
We test every model against our 12-benchmark suite covering tool calling, agentic planning, creative problem solving, safety calibration, and more. Each test is scored 1–5 by an LLM judge. Read our full methodology.