Is Claude Opus 4.6 better than Devstral Small 1.1?

In our testing Claude Opus 4.6 wins 9 of 12 benchmarks (strategic analysis, tool calling, long-context, safety, etc.). Devstral Small 1.1 wins classification. Claude is stronger for agentic, coding, and high-context tasks; Devstral is the cheaper classification specialist.

Which model is cheaper to run?

Devstral Small 1.1 is far cheaper. Per the payload: Claude charges $5/M input and $25/M output; Devstral charges $0.10/M input and $0.30/M output. A combined 1M input+1M output run costs $30 on Claude vs $0.40 on Devstral.

Which model is better for coding and math?

Claude Opus 4.6 is stronger: it wins tool calling and long-context in our tests and also scores 78.7% on SWE-bench Verified (Epoch AI) and 94.4% on AIME 2025 (Epoch AI), supporting its coding/math strengths.

Which model is better for classification tasks?

Devstral Small 1.1 wins classification in our suite (score 4 vs Claude's 3) and is tied for 1st in classification ranking (tied with 29 others), making it the preferred, lower-cost option for routing and tagging.

How does context window compare?

Claude Opus 4.6 has a 1,000,000-token context window; Devstral Small 1.1 has a 131,072-token window. For very long documents or multi-document retrieval, Claude is materially better in our long-context benchmark (5 vs 4).

Will switching to Devstral save money at scale?

Yes. Using the example of 10M input+10M output tokens/month: Claude would cost $300 while Devstral would cost $4. If you process millions of tokens monthly, Devstral reduces infrastructure costs dramatically; priceRatio in the payload is ~83.33×.

Claude Opus 4.6 vs Devstral Small 1.1

Claude Opus 4.6 is the practical winner for professional, agentic, and coding workflows: it wins 9 of 12 benchmarks including tool calling, long-context, and safety. Devstral Small 1.1 beats Opus only on classification and is the far cheaper option—best when cost and throughput matter more than top-tier reasoning.

anthropic

Claude Opus 4.6

Overall

4.58/5Strong

Benchmark Scores

Faithfulness

5/5

Long Context

5/5

Multilingual

5/5

Tool Calling

5/5

Classification

3/5

Agentic Planning

5/5

Structured Output

4/5

Safety Calibration

5/5

Strategic Analysis

5/5

Persona Consistency

5/5

Constrained Rewriting

3/5

Creative Problem Solving

5/5

External Benchmarks

SWE-bench Verified

78.7%

MATH Level 5

N/A

AIME 2025

94.4%

Pricing

Input

$5.00/MTok

Output

$25.00/MTok

Context Window1000K

modelpicker.net

mistral

Devstral Small 1.1

Overall

3.08/5Usable

Benchmark Scores

Faithfulness

4/5

Long Context

4/5

Multilingual

4/5

Tool Calling

4/5

Classification

4/5

Agentic Planning

2/5

Structured Output

4/5

Safety Calibration

2/5

Strategic Analysis

2/5

Persona Consistency

2/5

Constrained Rewriting

3/5

Creative Problem Solving

2/5

External Benchmarks

SWE-bench Verified

N/A

MATH Level 5

N/A

AIME 2025

N/A

Pricing

Input

$0.100/MTok

Output

$0.300/MTok

Context Window131K

modelpicker.net

Benchmark Analysis

In our 12-test suite Claude Opus 4.6 wins 9 categories, Devstral Small 1.1 wins 1, and 2 are ties. Detailed walk-through (score format: Claude vs Devstral, then rank context):

Strategic analysis: 5 vs 2. Claude tied for 1st (tied with 25 others of 54). That means Claude handles nuanced tradeoffs and numeric reasoning far better in tasks like pricing models or ROI analysis. Devstral ranks 44/54.
Creative problem solving: 5 vs 2. Claude tied for 1st (tied with 7 others). Expect more specific, feasible ideas from Claude; Devstral shows weaker performance here.
Agentic planning: 5 vs 2. Claude tied for 1st (tied with 14 others). Claude is better at goal decomposition and recovery for multi-step agents; Devstral ranks 53/54.
Tool calling: 5 vs 4. Claude tied for 1st (tied with 16 others); Devstral is mid-pack (rank 18/54). For function selection, sequencing, and argument accuracy, Claude is the safer choice.
Faithfulness: 5 vs 4. Claude tied for 1st (tied with 32 others); Devstral ranks 34/55. Claude is less likely to hallucinate or drift from sources in our tests.
Long context: 5 vs 4. Claude tied for 1st (tied with 36 others) and has a 1,000,000-token window versus Devstral's 131,072. Claude is markedly better for 30K+ retrieval and multi-document workflows.
Safety calibration: 5 vs 2. Claude tied for 1st (tied with 4 others); Devstral ranks 12/55. Claude more reliably refuses harmful prompts while permitting legitimate ones in our tests.
Persona consistency: 5 vs 2. Claude tied for 1st (tied with 36 others); Devstral ranks 51/53. Claude maintains character and resists prompt injection better.
Multilingual: 5 vs 4. Claude tied for 1st (tied with 34 others); Devstral ranks 36/55. Claude delivers higher parity in non-English output.
Classification: 3 vs 4 — Devstral wins this single category; Devstral is tied for 1st in classification (tied with 29 others of 53), making it the better, cheaper option for routing and tagging tasks.
Structured output: tie 4 vs 4. Both rank 26/54; both handle JSON/schema adherence similarly in our tests.
Constrained rewriting: tie 3 vs 3. Both rank 31/53; neither pulls ahead on hard compression tasks. External supplementary data: on SWE-bench Verified (Epoch AI) Claude Opus 4.6 scores 78.7%, and on AIME 2025 (Epoch AI) Claude scores 94.4% — these external results align with Claude's strength on coding and math-related tasks. Overall, Claude delivers materially higher capabilities for agentic, long-context, and safety-sensitive use cases; Devstral is the clear, inexpensive winner for classification and high-volume baseline workloads.

BenchmarkClaude Opus 4.6Devstral Small 1.1

Faithfulness5/54/5

Long Context5/54/5

Multilingual5/54/5

Tool Calling5/54/5

Classification3/54/5

Agentic Planning5/52/5

Structured Output4/54/5

Safety Calibration5/52/5

Strategic Analysis5/52/5

Persona Consistency5/52/5

Constrained Rewriting3/53/5

Creative Problem Solving5/52/5

Summary9 wins1 wins

Pricing Analysis

Pricing per million tokens: Claude Opus 4.6 charges $5 (input) + $25 (output) per M tokens; Devstral Small 1.1 charges $0.10 (input) + $0.30 (output) per M tokens. Example combined costs (1M input + 1M output): Claude = $30; Devstral = $0.40. Scale those linearly: 10M in+out → Claude $300 vs Devstral $4; 100M in+out → Claude $3,000 vs Devstral $40. The payload's priceRatio is 83.33, showing Claude is ~83× more expensive per token. Who should care: enterprises running heavy agentic workflows, code generation, or high-context document processing may accept Claude's cost for the quality and 1,000,000-token context window; startups, high-throughput classification services, and cost-sensitive consumer apps will prefer Devstral to cut expenses dramatically.

Real-World Cost Comparison

TaskClaude Opus 4.6Devstral Small 1.1

iChat response$0.014<$0.001

iBlog post$0.053<$0.001

iDocument batch$1.35$0.017

iPipeline run$13.50$0.170

Bottom Line

Choose Claude Opus 4.6 if you need best-in-class tool calling, long-context reasoning (1,000,000-token window), faithfulness, safety calibration, or multi-step agentic workflows — e.g., enterprise agents, code-generation pipelines, legal/medical multi-document analysis, or any workflow where mistakes are costly. Choose Devstral Small 1.1 if you need a massively cheaper model for high-throughput classification, simple chat or routing, and cost-constrained production (Devstral costs $0.40 per M tokens in+out versus Claude $30 per M in+out in the example). If you’re budget-constrained but only need solid classification or lightweight assistants, pick Devstral; if accuracy, safety, and long-context capabilities matter more than price, pick Claude.

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.