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Codestral 22B v0.1 vs Qwen3.7 Flash

At a Glance

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Pricing and Limits
Input priceFrom · USD / 1M tokensNot reported$0.030Openrouter ↗ · Sep 22, 2026
Output priceFrom · USD / 1M tokensNot reported$0.13Openrouter ↗ · Sep 22, 2026
Context windowMaximum documented tokens33K1,000K
Model facts checkedAug 28, 2026View model evidence →Sep 3, 2026View model evidence →

Token prices are the lowest available sourced USD rates; input and output may use different providers. Cost ranking estimates output spend on LiveBench, not a full request bill. Ranking methodology →

Available Benchmarks

All benchmark results →
No Protocol-Matched Benchmark Yet.Results appear here only when both models share the same benchmark version, metric, evaluation protocol, and evidence class.

Side-by-Side Facts

FieldCodestral-22B-v0.1Qwen3.7 Flash
DeveloperMistral AIQwen
FamilyCodestral 22b V0 1Qwen3 7
ModelCodestral-22B-v0.1Qwen3.7 Flash
VersionCodestral-22B-v0.1Qwen3.7 Flash
Lifecycleactiveactive
ReleasedUnknown2026-07-15
Knowledge cutoffUnknownUnknown
Input modalitiesTextText, Image, Video
Output modalitiesTextText
Context window33K1,000K
Total parameters22.2BUnknown
Active parametersUnknownUnknown
LicenseotherUnknown
Open weightsYesNo
API availableUnknownYes
Self-hostableYesNo
Provider accessUnknownAlibaba Cloud Model Studio (Standard), Openrouter (Standard)
Capabilitiesgenerationagents, chat, generation, reasoning, structured_outputs, tools, vision

Codestral 22B v0.1 Capabilities

generation
Serving providers0
Canonical IDmistralai/Codestral-22B-v0.1

Qwen3.7 Flash Capabilities

agentschatgenerationreasoningstructured outputstoolsvision
Serving providers2
Canonical IDqwen/qwen3.7-flash

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Primary Evidence

Sources and Freshness

mistralai/Codestral-22B-v0.1 developer model repositoryMistral AI · observed Aug 28, 2026Qwen3 VL Flash model and pricing | Alibaba Cloud Model StudioQwen · observed Sep 3, 2026

Questions

Codestral 22B v0.1 vs Qwen3.7 Flash FAQs

Is Codestral 22B v0.1 or Qwen3.7 Flash better for coding?+

This comparison does not currently contain a protocol-matched coding benchmark for both Codestral 22B v0.1 and Qwen3.7 Flash, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.

Which is cheaper, Codestral 22B v0.1 or Qwen3.7 Flash?+

Only Qwen3.7 Flash has a directly sourced input price: $0.030 per million tokens. Only Qwen3.7 Flash has a directly sourced output price: $0.13 per million tokens.

Which has a larger context window, Codestral 22B v0.1 or Qwen3.7 Flash?+

Qwen3.7 Flash has the larger sourced context window. Codestral 22B v0.1 supports 33K and Qwen3.7 Flash supports 1,000K.

Which performs better in benchmarks, Codestral 22B v0.1 or Qwen3.7 Flash?+

There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.

Can Codestral 22B v0.1 or Qwen3.7 Flash be self-hosted?+

Codestral 22B v0.1 is the only model in this pair currently marked as self-hostable. Codestral 22B v0.1 is open weight; Qwen3.7 Flash is not marked open weight.

Can Codestral 22B v0.1 and Qwen3.7 Flash understand images?+

Codestral 22B v0.1 is not documented with image input; Qwen3.7 Flash is documented with image input. This reflects supported input modalities, not vision quality.

Which can generate longer answers, Codestral 22B v0.1 or Qwen3.7 Flash?+

Neither has a larger sourced maximum output. Codestral 22B v0.1 is — and Qwen3.7 Flash is 66K.

Do Codestral 22B v0.1 and Qwen3.7 Flash support reasoning and tool use?+

Codestral 22B v0.1: none of these features are definitively sourced. Qwen3.7 Flash: reasoning, tool calling, and image input. Feature support does not establish relative quality.

Which is available from more inference providers, Codestral 22B v0.1 or Qwen3.7 Flash?+

Codestral 22B v0.1 has 0 sourced provider routes; Qwen3.7 Flash has 2, so Qwen3.7 Flash has broader tracked availability.

Which offers better value, Codestral 22B v0.1 or Qwen3.7 Flash?+

There is no universal value winner. Compare the input and output prices above with the matched benchmark result for your workload: cheaper tokens can be offset by different quality, token usage, latency, or provider availability.

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