GPT-5.6 Luna vs Qwen3.8 Max
At a Glance
| Compare |  GPT-5.6 LunaOpenAI |  Qwen3.8 MaxQwen |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| IntelligenceHigher is better · MM Intelligence v2.5 | #31 of 4652.7 score · 3/3 sources · complete | #10 of 4679.2 score · 2/3 sources · provisional · missing ARC-AGI-2 · full-core range 52.8–86.1 |
| CostLower is better · Published-token output estimate | #6 of 44$0.026 per LiveBench case | #23 of 44$0.107 per LiveBench case |
| EfficiencyHigher is better · MM Efficiency v1.5 | #4 of 3864.8 score · 3/3 sources · complete | #6 of 3863.2 score · 2/3 sources · provisional · missing ARC-AGI-2 · full-core range 50.0–66.7 |
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $0.20Openai ↗ · Sep 3, 2026 | $1.65Deepinfra ↗ · Sep 21, 2026 |
| Output priceFrom · USD / 1M tokens | $1.20Openai ↗ · Sep 3, 2026 | $4.951Deepinfra ↗ · Sep 21, 2026 |
| Context windowMaximum documented tokens | 1,050K | 1,000K |
| Model facts checked | Aug 28, 2026View model evidence → | Aug 29, 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
| Benchmark | GPT-5.6 Luna | Qwen3.8-Max |
|---|---|---|
| LMArena Agent Arenaagent-2026-09-15-d25aabda0010 · outcome_score · leader | -0.4496% of row best · score · GPT 5.6 Luna (xHigh); 95% CI [-1.27310649, 0.39719069]; sessions 29186; observations 2307801; rank 27 | 3.30100% of row best · score · Qwen3.8 Max; 95% CI [2.46604139, 4.14181105]; sessions 31489; observations 2502655; rank 17 |
| LMArena Text Arenatext-2026-09-13-d25aabda0010 · arena_rating · leader | 1,429.8997% of row best · rating · gpt-5.6-luna-xhigh; 95% CI [1425.11549148, 1434.66063238]; votes 28547; rank 86 | 1,480.55100% of row best · rating · qwen3.8-max; 95% CI [1474.79040793, 1486.31846927]; votes 16670; rank 14 |
| LMArena Vision Arenavision-2026-09-13-d25aabda0010 · arena_rating · leader | 1,258.5396% of row best · rating · gpt-5.6-luna-xhigh; 95% CI [1250.17799393, 1266.87249678]; votes 7793; rank 48 | 1,315.33100% of row best · rating · qwen3.8-max; 95% CI [1307.40892164, 1323.25970514]; votes 8665; rank 6 |
| LiveBench2026-06-25 · overall · leader | 77.0594% of row best · percent · gpt-5.6-luna-max · 21,799 output tokens / case | 81.88100% of row best · percent · qwen3.8-max · 21,637 output tokens / case |
| ToneBench2026-09-11-10-task-4ef099199c9c · overall_score · unverified | 84.47100% of row best · points · GPT-5.6 Luna (ultra) · 17,605 output tokens / case | 83.3299% of row best · points · Qwen3.8 Max · 22,888 output tokens / case |
| Overall ResultCounted from the protocol-matched rows above | 0 benchmark wins | 4 benchmark winsOverall lead |
Third-party benchmark Only like-for-like primary-publisher results are shown; raw scores, relative scores, configuration, and token spend remain visible.
Side-by-Side Facts
| Field | GPT-5.6 Luna | Qwen3.8-Max |
|---|---|---|
| Developer | OpenAI | Qwen |
| Family | Gpt 5 6 | Qwen3 8 Max |
| Model | GPT-5.6 Luna | Qwen3.8-Max |
| Version | GPT-5.6 Luna | Qwen3.8-Max |
| Lifecycle | active | active |
| Released | Unknown | Unknown |
| 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 | 2.4T |
| 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), Fireworks Ai (Standard), Openrouter (Standard) |
| Capabilities | chat, generation, reasoning, tools | agents, chat, reasoning, structured_outputs, tools, vision |
GPT-5.6 Luna Capabilities
Qwen3.8 Max Capabilities
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Primary Evidence
Sources and Freshness
Questions
GPT-5.6 Luna vs Qwen3.8 Max FAQs
Is GPT-5.6 Luna or Qwen3.8 Max better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both GPT-5.6 Luna and Qwen3.8 Max, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, GPT-5.6 Luna or Qwen3.8 Max?+
GPT-5.6 Luna is $0.20 and Qwen3.8 Max is $1.65 per million tokens, so GPT-5.6 Luna is cheaper on this metric. GPT-5.6 Luna is $1.20 and Qwen3.8 Max is $4.951 per million tokens, so GPT-5.6 Luna is cheaper on this metric.
Which has a larger context window, GPT-5.6 Luna or Qwen3.8 Max?+
GPT-5.6 Luna has the larger sourced context window. GPT-5.6 Luna supports 1,050K and Qwen3.8 Max supports 1,000K.
Which performs better in benchmarks, GPT-5.6 Luna or Qwen3.8 Max?+
Qwen3.8 Max leads the current overall benchmark count. The result uses 4 protocol-matched benchmarks from 2 publishers; it is not a universal quality score.
Can GPT-5.6 Luna or Qwen3.8 Max be self-hosted?+
Both models have the same recorded self-hosting status: unsupported. GPT-5.6 Luna is not marked open weight; Qwen3.8 Max is not marked open weight.
Can GPT-5.6 Luna and Qwen3.8 Max understand images?+
GPT-5.6 Luna is documented with image input; Qwen3.8 Max is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, GPT-5.6 Luna or Qwen3.8 Max?+
Qwen3.8 Max has the larger sourced maximum output: GPT-5.6 Luna supports 128K and Qwen3.8 Max supports 131K output tokens.
Do GPT-5.6 Luna and Qwen3.8 Max support reasoning and tool use?+
GPT-5.6 Luna: reasoning, tool calling, and image input. Qwen3.8 Max: reasoning, tool calling, and image input. Feature support does not establish relative quality.
Which is available from more inference providers, GPT-5.6 Luna or Qwen3.8 Max?+
GPT-5.6 Luna has 2 sourced provider routes; Qwen3.8 Max has 4, so Qwen3.8 Max has broader tracked availability.
Which offers better value, GPT-5.6 Luna or Qwen3.8 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.