Qwen3.5 397B A17B vs GPT-5.2 Pro

At a Glance

Compare
Pricing and Limits
Input priceFrom · USD / 1M tokens$0.45Deepinfra · Sep 21, 2026$21.00Openai · Sep 3, 2026
Output priceFrom · USD / 1M tokens$3.00Deepinfra · Sep 21, 2026$168.00Openai · Sep 3, 2026
Context windowMaximum documented tokens262K400K
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

FieldQwen3.5-397B-A17BGPT-5.2 Pro
DeveloperQwenOpenAI
FamilyQwen3 5 397b A17bGpt 5 2
ModelQwen3.5-397B-A17BGPT-5.2 Pro
VersionQwen3.5-397B-A17BGPT-5.2 Pro
Lifecycleactiveactive
Released2026-02-152025-12-11
Knowledge cutoffUnknown2025-08-31
Input modalitiesText, ImageText, Image
Output modalitiesTextText
Context window262K400K
Total parameters403.4BUnknown
Active parameters17BUnknown
Licenseapache-2.0Unknown
Open weightsYesNo
API availableYesYes
Self-hostableYesNo
Provider accessDeepinfra (Standard), Hugging Face (Standard), Openrouter (Standard), Together Ai (Standard)Openai (Standard), Openrouter (Standard)
Capabilitieschat, generation, reasoning, toolschat, generation, reasoning, structured_outputs, tools

Qwen3.5 397B A17B Capabilities

chatgenerationreasoningtools
Serving providers4
Canonical IDQwen/Qwen3.5-397B-A17B

GPT-5.2 Pro Capabilities

chatgenerationreasoningstructured outputstools
Serving providers2
Canonical IDopenai/gpt-5.2-pro

Primary Evidence

Sources and Freshness

Questions

Qwen3.5 397B A17B vs GPT-5.2 Pro FAQs

Is Qwen3.5 397B A17B or GPT-5.2 Pro better for coding?+

This comparison does not currently contain a protocol-matched coding benchmark for both Qwen3.5 397B A17B and GPT-5.2 Pro, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.

Which is cheaper, Qwen3.5 397B A17B or GPT-5.2 Pro?+

Qwen3.5 397B A17B is $0.45 and GPT-5.2 Pro is $21.00 per million tokens, so Qwen3.5 397B A17B is cheaper on this metric. Qwen3.5 397B A17B is $3.00 and GPT-5.2 Pro is $168.00 per million tokens, so Qwen3.5 397B A17B is cheaper on this metric.

Which has a larger context window, Qwen3.5 397B A17B or GPT-5.2 Pro?+

GPT-5.2 Pro has the larger sourced context window. Qwen3.5 397B A17B supports 262K and GPT-5.2 Pro supports 400K.

Which performs better in benchmarks, Qwen3.5 397B A17B or GPT-5.2 Pro?+

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

Can Qwen3.5 397B A17B or GPT-5.2 Pro be self-hosted?+

Qwen3.5 397B A17B is the only model in this pair currently marked as self-hostable. Qwen3.5 397B A17B is open weight; GPT-5.2 Pro is not marked open weight.

Can Qwen3.5 397B A17B and GPT-5.2 Pro understand images?+

Qwen3.5 397B A17B is documented with image input; GPT-5.2 Pro is documented with image input. This reflects supported input modalities, not vision quality.

Which can generate longer answers, Qwen3.5 397B A17B or GPT-5.2 Pro?+

Neither has a larger sourced maximum output. Qwen3.5 397B A17B is — and GPT-5.2 Pro is 128K.

Do Qwen3.5 397B A17B and GPT-5.2 Pro support reasoning and tool use?+

Qwen3.5 397B A17B: reasoning, tool calling, and image input. GPT-5.2 Pro: reasoning, tool calling, and image input. Feature support does not establish relative quality.

Which is available from more inference providers, Qwen3.5 397B A17B or GPT-5.2 Pro?+

Qwen3.5 397B A17B has 4 sourced provider routes; GPT-5.2 Pro has 2, so Qwen3.5 397B A17B has broader tracked availability.

Which offers better value, Qwen3.5 397B A17B or GPT-5.2 Pro?+

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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