Devstral 2 2512 vs Gemma 4 26B A4B
In our testing Gemma 4 26B A4B is the better all-round pick: it wins 5 of 12 benchmarks (tool_calling, faithfulness, classification, strategic_analysis, persona_consistency) and is far cheaper. Devstral 2 2512 wins constrained_rewriting and matches or ties on several long-context and structured-output tasks, but costs ~5.7× more per output token ($2.00 vs $0.35).
mistral
Devstral 2 2512
Benchmark Scores
External Benchmarks
Pricing
Input
$0.400/MTok
Output
$2.00/MTok
modelpicker.net
Gemma 4 26B A4B
Benchmark Scores
External Benchmarks
Pricing
Input
$0.080/MTok
Output
$0.350/MTok
modelpicker.net
Benchmark Analysis
Summary of head-to-head results in our 12-test suite (scores 1–5):
- Gemma wins (5 benchmarks): strategic_analysis 5 (tied for 1st of 54), tool_calling 5 (tied for 1st of 54), faithfulness 5 (tied for 1st of 55), classification 4 (tied for 1st of 53), persona_consistency 5 (tied for 1st of 53). These wins indicate Gemma is stronger at nuanced tradeoff reasoning, function selection/argument accuracy, sticking to source material, accurate routing, and maintaining persona.
- Devstral wins (1 benchmark): constrained_rewriting 5 (tied for 1st of 53) versus Gemma 3 (rank 31). This shows Devstral is superior when you must compress or rewrite within hard character limits.
- Ties (6 benchmarks): structured_output both 5 (tied for 1st), creative_problem_solving both 4 (rank 9), long_context both 5 (tied for 1st), safety_calibration both 1 (rank 32), agentic_planning both 4 (rank 16), multilingual both 5 (tied for 1st). Practically: both models handle very long contexts and multilingual output equally well in our tests and both produce excellent structured outputs, but neither scores well on safety_calibration. Context & task implications: Gemma’s 5/5 in tool_calling and 5/5 faithfulness mean it will more reliably select and sequence functions and adhere to source texts — valuable for production automation and retrieval-augmented workflows. Devstral’s top score in constrained_rewriting makes it the clear choice for tight-format transformations (e.g., summarizing to strict character-limited channels). Both models share a 262,144 token context window; Gemma supports text+image+video->text while Devstral is text->text, which matters if you need multimodal inputs.
Pricing Analysis
Per-mTok pricing: Devstral 2 2512 charges $0.40 input / $2.00 output; Gemma 4 26B A4B charges $0.08 input / $0.35 output. For 1 million tokens (1000 mTok) of input+output (1:1): Gemma = $80 (input) + $350 (output) = $430; Devstral = $400 + $2,000 = $2,400. For 10M tokens: Gemma $4,300 vs Devstral $24,000. For 100M tokens: Gemma $43,000 vs Devstral $240,000. Teams with high-volume inference (chatbots, large-scale pipelines) should care: Devstral increases monthly spend by about $1,970 per 1M tokens and by $196,000 per 100M tokens compared with Gemma, so Gemma is materially preferable when cost per token matters.
Real-World Cost Comparison
Bottom Line
Choose Gemma 4 26B A4B if you need lower-cost production inference, stronger tool calling, higher faithfulness, better classification and persona consistency — it won 5 of 12 benchmarks and costs $0.35/output mTok ($350 per 1M output tokens). Choose Devstral 2 2512 if your priority is constrained_rewriting and you need the specific strengths indicated by its 5/5 constrained_rewriting score, and you can accept a much higher run cost ($2.00/output mTok). If you need multimodal inputs (images/video), prefer Gemma; if you must squeeze outputs into tight limits, prefer Devstral.
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.