Pixtral Large vs Qwen3.7 Max

At a Glance

Compare
Pixtral LargeMistral AI
Intelligence, Cost, and Efficiency
CostLower is better · Published-token output estimateUnrankedNot in the 44-model eligible cohort#13 of 44$0.057 per LiveBench case
Pricing and Limits
Input priceFrom · USD / 1M tokensNot reported$1.475Openrouter · Sep 22, 2026
Output priceFrom · USD / 1M tokensNot reported$4.425Openrouter · Sep 22, 2026
Context windowMaximum documented tokens131K1,000K
Model facts checkedAug 29, 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

FieldPixtral LargeQwen3.7 Max
DeveloperMistral AIQwen
FamilyPixtral LargeQwen3 7
ModelPixtral LargeQwen3.7 Max
VersionPixtral LargeQwen3.7 Max
Lifecycledeprecatedactive
Released2024-11-182026-05-20
Knowledge cutoffUnknownUnknown
Input modalitiesText, Image, DocumentText, Image, Video
Output modalitiesTextText
Context window131K1,000K
Total parametersUnknownUnknown
Active parametersUnknownUnknown
LicenseUnknownUnknown
Open weightsNoNo
API availableNoYes
Self-hostableNoNo
Provider accessUnknownAlibaba Cloud Model Studio (Standard), Deepinfra (Standard), Openrouter (Standard), Together Ai (Standard)
Capabilitieschat, generation, structured_outputs, tools, visionagents, chat, generation, reasoning, structured_outputs, tools, vision

Pixtral Large Capabilities

chatgenerationstructured outputstoolsvision
Serving providers0
Canonical IDmistralai/pixtral-large-2411

Qwen3.7 Max Capabilities

agentschatgenerationreasoningstructured outputstoolsvision
Serving providers4
Canonical IDqwen/qwen3.7-max

Primary Evidence

Sources and Freshness

Questions

Pixtral Large vs Qwen3.7 Max FAQs

Is Pixtral Large or Qwen3.7 Max better for coding?+

This comparison does not currently contain a protocol-matched coding benchmark for both Pixtral Large and Qwen3.7 Max, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.

Which is cheaper, Pixtral Large or Qwen3.7 Max?+

Only Qwen3.7 Max has a directly sourced input price: $1.475 per million tokens. Only Qwen3.7 Max has a directly sourced output price: $4.425 per million tokens.

Which has a larger context window, Pixtral Large or Qwen3.7 Max?+

Qwen3.7 Max has the larger sourced context window. Pixtral Large supports 131K and Qwen3.7 Max supports 1,000K.

Which performs better in benchmarks, Pixtral Large or Qwen3.7 Max?+

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

Can Pixtral Large or Qwen3.7 Max be self-hosted?+

Both models have the same recorded self-hosting status: unsupported. Pixtral Large is not marked open weight; Qwen3.7 Max is not marked open weight.

Can Pixtral Large and Qwen3.7 Max understand images?+

Pixtral Large is documented with image input; Qwen3.7 Max is documented with image input. This reflects supported input modalities, not vision quality.

Which can generate longer answers, Pixtral Large or Qwen3.7 Max?+

Neither has a larger sourced maximum output. Pixtral Large is — and Qwen3.7 Max is 66K.

Do Pixtral Large and Qwen3.7 Max support reasoning and tool use?+

Pixtral Large: tool calling and image input. Qwen3.7 Max: reasoning, tool calling, and image input. Feature support does not establish relative quality.

Which is available from more inference providers, Pixtral Large or Qwen3.7 Max?+

Pixtral Large has 0 sourced provider routes; Qwen3.7 Max has 4, so Qwen3.7 Max has broader tracked availability.

Which offers better value, Pixtral Large or Qwen3.7 Max?+

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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