GPT-5.6 Sol vs Qwen3.7 Max
Model Markets Rankings
Intelligence, Cost, and Efficiency
| Ranking | GPT-5.6 Sol | Qwen3.7 Max |
|---|---|---|
| IntelligenceHigher is better · MM Intelligence v1.2 | #4 of 2285.2 score | UnrankedNot in the 22-model eligible cohort |
| CostLower is better · Published-token output estimate | #20 of 36$0.117 per LiveBench case | #12 of 36$0.057 per LiveBench case |
| EfficiencyHigher is better · MM Efficiency v1.0 | #3 of 2263.6 score | UnrankedNot in the 22-model eligible cohort |
Ranks come from the current complete eligible cohorts. Green highlights appear only when both models are ranked in the same metric. Missing required inputs remain unranked, and the three dimensions are not collapsed into an overall winner.
Benchmark Performance
Available Benchmarks
| Benchmark | GPT-5.6 Sol | Qwen3.7 Max |
|---|---|---|
| LMArena Agent Arenaagent-2026-08-31-011508720696 · outcome_score · leader | 9.76100% of row best · score · GPT 5.6 Sol (xHigh); 95% CI [8.23209281, 11.28769899]; sessions 28524; observations 2917301; rank 4 | -1.3690% of row best · score · Qwen3.7 Max; 95% CI [-2.27613811, -0.44192410]; sessions 35069; observations 1551158; rank 35 |
| LiveBench2026-06-25 · overall · leader | 85.26100% of row best · percent · gpt-5.6-sol-max · 11,729 output tokens / case | 77.5091% of row best · percent · qwen3.7-max · 12,909 output tokens / case |
| ToneBench2026-08-28-10-task-cd9819ab6e4d · overall_score · leader | 87.35100% of row best · points · GPT-5.6 Sol (default) · 2,955 output tokens / case | 81.2693% of row best · points · Qwen3.7 Max (default) · 8,636 output tokens / case |
| Overall ResultCounted from the protocol-matched rows above | 3 benchmark winsOverall lead | 0 benchmark wins |
Third-party benchmark Only like-for-like primary-publisher results are shown; raw scores, relative scores, configuration, and token spend remain visible.
Technical Differences
Side-by-Side Facts
| Field | GPT-5.6 Sol | Qwen3.7 Max |
|---|---|---|
| Developer | OpenAI | Qwen |
| Family | Gpt 5 6 | Qwen3 7 |
| Model | GPT-5.6 Sol | Qwen3.7 Max |
| Version | GPT-5.6 Sol | Qwen3.7 Max |
| Lifecycle | active | active |
| Released | Unknown | 2026-05-20 |
| Knowledge cutoff | 2026-02-16 | Unknown |
| Input modalities | Text, Image | Text, Image, Video |
| Output modalities | Text | Text |
| Context window | 1,050K | 1,000K |
| Total parameters | Unknown | Unknown |
| Active parameters | Unknown | Unknown |
| License | Unknown | Unknown |
| Open weights | No | No |
| API available | Yes | Yes |
| Self-hostable | No | No |
| Provider access | Openai (Standard), Openrouter (Standard) | Alibaba Cloud Model Studio (Standard), Deepinfra (Standard), Openrouter (Standard), Together Ai (Standard) |
| Capabilities | chat, generation, reasoning, tools | agents, chat, generation, reasoning, structured_outputs, tools, vision |
13 comparable fields · 8 material differences · Pair passes the primary-source comparison gate
GPT-5.6 Sol Capabilities
Qwen3.7 Max Capabilities
Internal Comparison Graph
Related Comparisons
| A | Pair | B | Context |
|---|---|---|---|
GPT-5.6 SolOpenAI | vs | GPT-6 AstraOpenAI | family variantsimage, text |
Claude Fable 5.1Anthropic | vs | GPT-5.6 SolOpenAI | cross-developer peersimage, text |
Gemini 3.1 ProGoogle DeepMind | vs | GPT-5.6 SolOpenAI | cross-developer peerstext |
DeepSeek-V4-ProDeepSeek | vs | GPT-5.6 SolOpenAI | cross-developer peerstext |
GPT-5.6 SolOpenAI | vs | Grok 4.6xAI | cross-developer peersimage, text |
GPT-5.6 SolOpenAI | vs | Qwen3.8-MaxQwen | cross-developer peersimage, text |
Kimi-K3Moonshot AI | vs | GPT-5.6 SolOpenAI | cross-developer peersimage, text |
MiniMax-M3MiniMax | vs | GPT-5.6 SolOpenAI | cross-developer peersimage, text |
GPT-5.6 SolOpenAI | vs | GLM-5.3Z.ai | cross-developer peerstext |
GPT-5.6 SolOpenAI | vs | Hy4 previewTencent | cross-developer peerstext |
Seed 2.1 ProByteDance Seed | vs | GPT-5.6 SolOpenAI | cross-developer peersimage, text |
Mistral Large 3Mistral AI | vs | GPT-5.6 SolOpenAI | cross-developer peersimage, text |
Primary Evidence
Sources and Freshness
Questions
GPT-5.6 Sol vs Qwen3.7 Max FAQs
Is GPT-5.6 Sol or Qwen3.7 Max better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both GPT-5.6 Sol and Qwen3.7 Max, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, GPT-5.6 Sol or Qwen3.7 Max?+
GPT-5.6 Sol is $2.00 and Qwen3.7 Max is $1.475 per million tokens, so Qwen3.7 Max is cheaper on this metric. GPT-5.6 Sol is $10.00 and Qwen3.7 Max is $4.425 per million tokens, so Qwen3.7 Max is cheaper on this metric.
Which has a larger context window, GPT-5.6 Sol or Qwen3.7 Max?+
GPT-5.6 Sol has the larger sourced context window. GPT-5.6 Sol supports 1,050K and Qwen3.7 Max supports 1,000K.
Which performs better in benchmarks, GPT-5.6 Sol or Qwen3.7 Max?+
GPT-5.6 Sol leads the current overall benchmark count. The result uses 3 protocol-matched benchmarks from 3 publishers; it is not a universal quality score.
Can GPT-5.6 Sol or Qwen3.7 Max be self-hosted?+
Both models have the same recorded self-hosting status: unsupported. GPT-5.6 Sol is not marked open weight; Qwen3.7 Max is not marked open weight.
Can GPT-5.6 Sol and Qwen3.7 Max understand images?+
GPT-5.6 Sol 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, GPT-5.6 Sol or Qwen3.7 Max?+
GPT-5.6 Sol has the larger sourced maximum output: GPT-5.6 Sol supports 128K and Qwen3.7 Max supports 66K output tokens.
Do GPT-5.6 Sol and Qwen3.7 Max support reasoning and tool use?+
GPT-5.6 Sol: reasoning, 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, GPT-5.6 Sol or Qwen3.7 Max?+
GPT-5.6 Sol has 2 sourced provider routes; Qwen3.7 Max has 4, so Qwen3.7 Max has broader tracked availability.
Which offers better value, GPT-5.6 Sol 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.