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Curated head-to-head

Mistral Small 3.1 vs DeepSeek R1

A workload-first comparison—not a universal winner. Review price, access, context, and evidence before choosing.

01 / Open weights
by Mistral AI

Mistral Small 3.1

A compact multimodal model aimed at low-latency assistants and local deployment. Its Apache license and moderate size make it unusually practical for customization.

open-weightsvision
CONTEXT128K
LICENSEApache 2.0
02 / Open weights
by DeepSeek

DeepSeek R1

A mixture-of-experts reasoning model released with permissive weights. R1 made high-end chain-of-thought style performance more accessible to self-hosted teams.

reasoningopen-weights
CONTEXT131K
LICENSEMIT

The meaningful difference

Mistral is dramatically easier to host; full R1 demands far more infrastructure but targets harder reasoning tasks.

Choose by workload

Choose Mistral Small 3.1 when your priority is efficient local multimodal assistants. Its relevant strengths include apache-licensed weights and fits on a single high-memory gpu when quantized.

Choose DeepSeek R1 when your priority is maximum open-weight reasoning performance. Account for full model has demanding infrastructure needs before committing.

Specification comparison

MeasureMistral Small 3.1DeepSeek R1
AccessOpen weightsOpen weights
LicenseApache 2.0MIT
Context128K131K
Provider-listed API input / 1M$0.10$0.55
Provider-listed API output / 1M$0.30$2.19
MMLU-Pro66.884
GPQA Diamond46.871.5
SWE-bench Verified—49.2
LiveCodeBench32.165.9
SWE-Bench Pro——
Artificial Analysis Intelligence Index——

A fair test for this pair

Start with Mistral and an R1 distilled variant on your actual hardware before considering the full R1 serving cost.

Bottom line: use reported results to form a hypothesis, then make the decision with representative private tasks. Missing scores remain missing; no composite winner is manufactured.