Qwen3.8 27B vs GPT-5.6 Luna
At a Glance
| Compare |  Qwen3.8 27BQwen |  GPT-5.6 LunaOpenAI |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| IntelligenceHigher is better · MM Intelligence v2.5 | #30 of 4654.0 score · 2/3 sources · provisional · missing ARC-AGI-2 · full-core range 36.0–69.4 | #31 of 4652.7 score · 3/3 sources · complete |
| CostLower is better · Published-token output estimate | #16 of 44$0.072 per LiveBench case | #6 of 44$0.026 per LiveBench case |
| EfficiencyHigher is better · MM Efficiency v1.5 | #17 of 3854.8 score · 2/3 sources · provisional · missing ARC-AGI-2 · full-core range 45.8–62.5 | #4 of 3864.8 score · 3/3 sources · complete |
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $0.20Deepinfra ↗ · Sep 22, 2026 | $0.20Openai ↗ · Sep 3, 2026 |
| Output priceFrom · USD / 1M tokens | $2.50Deepinfra ↗ · Sep 22, 2026 | $1.20Openai ↗ · Sep 3, 2026 |
| Context windowMaximum documented tokens | 262K | 1,050K |
| Model facts checked | Aug 28, 2026View model evidence → | Aug 28, 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 | Qwen3.8-27B | GPT-5.6 Luna |
|---|---|---|
| LMArena Agent Arenaagent-2026-09-15-d25aabda0010 · outcome_score · statistical tie | -0.64100% of row best · score · Qwen 3.8 27B; 95% CI [-1.36495564, 0.07819043]; sessions 37257; observations 4349219; rank 29 | -0.44100% of row best · score · GPT 5.6 Luna (xHigh); 95% CI [-1.27310649, 0.39719069]; sessions 29186; observations 2307801; rank 27 |
| LMArena Text Arenatext-2026-09-13-d25aabda0010 · arena_rating · statistical tie | 1,439.27100% of row best · rating · qwen3.8-27b; 95% CI [1432.84410050, 1445.69542028]; votes 10697; rank 68 | 1,429.8999% of row best · rating · gpt-5.6-luna-xhigh; 95% CI [1425.11549148, 1434.66063238]; votes 28547; rank 86 |
| LMArena Vision Arenavision-2026-09-13-d25aabda0010 · arena_rating · statistical tie | 1,272.12100% of row best · rating · qwen3.8-27b; 95% CI [1262.21853873, 1282.01179703]; votes 4577; rank 35 | 1,258.5399% of row best · rating · gpt-5.6-luna-xhigh; 95% CI [1250.17799393, 1266.87249678]; votes 7793; rank 48 |
| LiveBench2026-06-25 · overall · leader | 78.02100% of row best · percent · qwen3.8-27b · 28,740 output tokens / case | 77.0599% of row best · percent · gpt-5.6-luna-max · 21,799 output tokens / case |
| Overall ResultCounted from the protocol-matched rows above · 3 ties | 1 benchmark winNo overall winner | 0 benchmark winsNo overall winner |
Third-party benchmark Only like-for-like primary-publisher results are shown; raw scores, relative scores, configuration, and token spend remain visible.
Quality Versus Estimated Output Cost
Side-by-Side Facts
| Field | Qwen3.8-27B | GPT-5.6 Luna |
|---|---|---|
| Developer | Qwen | OpenAI |
| Family | Qwen3 8 27b | Gpt 5 6 |
| Model | Qwen3.8-27B | GPT-5.6 Luna |
| Version | Qwen3.8-27B | GPT-5.6 Luna |
| Lifecycle | active | active |
| Released | Unknown | Unknown |
| Knowledge cutoff | Unknown | 2026-02-16 |
| Input modalities | Text, Image | Text, Image |
| Output modalities | Text | Text |
| Context window | 262K | 1,050K |
| Total parameters | 27.8B | Unknown |
| Active parameters | Unknown | Unknown |
| License | apache-2.0 | Unknown |
| Open weights | Yes | No |
| API available | Yes | Yes |
| Self-hostable | Yes | No |
| Provider access | Deepinfra (Standard), Hugging Face (Standard), Openrouter (Standard) | Openai (Standard), Openrouter (Standard) |
| Capabilities | chat, generation, reasoning, tools | chat, generation, reasoning, tools |
Qwen3.8 27B Capabilities
GPT-5.6 Luna Capabilities
Related Comparisons
All image comparisons →| A | Pair | B | Context |
|---|---|---|---|
Qwen3.8-27BQwen | vs | GPT-6 AstraOpenAI | cross-developer peersimage, text |
Qwen3.8-27BQwen | vs | Claude Fable 5.1Anthropic | cross-developer peersimage, text |
Qwen3.8-27BQwen | vs |  Gemini 3.1 ProGoogle DeepMind | cross-developer peerstext |
Qwen3.8-27BQwen | vs |  DeepSeek-V4.1-FlashDeepSeek | cross-developer peersimage, text |
Qwen3.8-27BQwen | vs | Grok 4.6xAI | cross-developer peersimage, text |
Qwen3.8-27BQwen | vs | Qwen3.8-MaxQwen | family variantsimage, text |
Qwen3.8-27BQwen | vs |  Kimi-K3Moonshot AI | cross-developer peersimage, text |
 MiniMax-M3MiniMax | vs | Qwen3.8-27BQwen | cross-developer peersimage, text |
Qwen3.8-27BQwen | vs |  GLM-5.3Z.ai | cross-developer peerstext |
Qwen3.8-27BQwen | vs |  Hy4 previewTencent | cross-developer peerstext |
Qwen3.8-27BQwen | vs | Seed 2.1 ProByteDance Seed | cross-developer peersimage, text |
Qwen3.8-27BQwen | vs |  Mistral Large 3Mistral AI | cross-developer peersimage, text |
Primary Evidence
Sources and Freshness
Questions
Qwen3.8 27B vs GPT-5.6 Luna FAQs
Is Qwen3.8 27B or GPT-5.6 Luna better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Qwen3.8 27B and GPT-5.6 Luna, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Qwen3.8 27B or GPT-5.6 Luna?+
Qwen3.8 27B is $0.20 and GPT-5.6 Luna is $0.20 per million tokens, so they are tied on this metric. Qwen3.8 27B is $2.50 and GPT-5.6 Luna is $1.20 per million tokens, so GPT-5.6 Luna is cheaper on this metric.
Which has a larger context window, Qwen3.8 27B or GPT-5.6 Luna?+
GPT-5.6 Luna has the larger sourced context window. Qwen3.8 27B supports 262K and GPT-5.6 Luna supports 1,050K.
Which performs better in benchmarks, Qwen3.8 27B or GPT-5.6 Luna?+
There is no overall benchmark winner: An overall winner requires at least two decisive benchmarks from at least two original publishers.
Can Qwen3.8 27B or GPT-5.6 Luna be self-hosted?+
Qwen3.8 27B is the only model in this pair currently marked as self-hostable. Qwen3.8 27B is open weight; GPT-5.6 Luna is not marked open weight.
Can Qwen3.8 27B and GPT-5.6 Luna understand images?+
Qwen3.8 27B is documented with image input; GPT-5.6 Luna is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Qwen3.8 27B or GPT-5.6 Luna?+
Qwen3.8 27B has the larger sourced maximum output: Qwen3.8 27B supports 131K and GPT-5.6 Luna supports 128K output tokens.
Do Qwen3.8 27B and GPT-5.6 Luna support reasoning and tool use?+
Qwen3.8 27B: reasoning, tool calling, and image input. GPT-5.6 Luna: reasoning, tool calling, and image input. Feature support does not establish relative quality.
Which is available from more inference providers, Qwen3.8 27B or GPT-5.6 Luna?+
Qwen3.8 27B has 3 sourced provider routes; GPT-5.6 Luna has 2, so Qwen3.8 27B has broader tracked availability.
Which offers better value, Qwen3.8 27B or GPT-5.6 Luna?+
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.