GPT-6 Luna vs Qwen3.8 Flash
At a Glance
| Compare |  GPT-6 LunaOpenAI |  Qwen3.8 FlashQwen |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| IntelligenceHigher is better · MM Intelligence v4.4 | #29 of 5742.3 score · 3/3 sources · complete | #15 of 5767.5 score · 1/3 sources · single-source provisional · missing ARC-AGI-2, LMArena Text · full-core range 22.5–89.2 |
| CostLower is better · Published-token output estimate | #4 of 27$0.018 per LiveBench case | #5 of 27$0.021 per LiveBench case |
| EfficiencyHigher is better · MM Efficiency v1.9 | #10 of 2752.8 score · 3/3 sources · complete | #3 of 2764.2 score · 1/3 sources · single-source provisional · missing ARC-AGI-2, LMArena Text · full-core range 41.7–75.0 |
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $0.10Openai ↗ · Sep 22, 2026 | $0.113Deepinfra ↗ · Oct 19, 2026 |
| Output priceFrom · USD / 1M tokens | $0.50Openai ↗ · Sep 22, 2026 | $0.382Deepinfra ↗ · Oct 19, 2026 |
| Context windowMaximum documented tokens | 1,050K | 1,000K |
| Model facts checked | Sep 22, 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-6 Luna | Qwen3.8-Flash |
|---|---|---|
| LiveBench2026-06-25-90f8d44da965 · overall · leader | 75.8496% of row best · percent · gpt-6-luna-max · 35,869 output tokens / case | 79.12100% of row best · percent · qwen3.8-flash-next · 54,438 output tokens / case |
| ToneBench2026-10-17-10-task-e9c76358afb2 · overall_score · unverified | 78.19100% of row best · points · GPT-6 Luna (max) · 6,595 output tokens / case | 75.3096% of row best · points · Qwen3.8 Flash (thinking on) · 31,590 output tokens / case |
| Overall ResultCounted from the protocol-matched rows above | 0 benchmark winsNo overall winner | 1 benchmark winNo overall winner |
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-6 Luna | Qwen3.8-Flash |
|---|---|---|
| Developer | OpenAI | Qwen |
| Family | Gpt 6 | Qwen3 8 Flash |
| Model | GPT-6 Luna | Qwen3.8-Flash |
| Version | GPT-6 Luna | Qwen3.8-Flash |
| Lifecycle | active | active |
| Released | 2026-09-22 | Unknown |
| Knowledge cutoff | 2026-05-18 | 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, computer-use, generation, reasoning, tools | agents, chat, computer-use, reasoning, structured_outputs, tools, vision |
| Default snapshot | gpt-6-luna | Unknown |
| Maximum input tokens | 922000 | Unknown |
| Reasoning effort | none, low, medium, high, xhigh, max | Unknown |
| Supported API routes | v1/responses, v1/chat/completions, v1/batch | Unknown |
GPT-6 Luna Capabilities
Qwen3.8 Flash Capabilities
Related Comparisons
All image comparisons →| A | Pair | B | Context |
|---|---|---|---|
GPT-5.6 LunaOpenAI | vs | GPT-6 LunaOpenAI | family variantsimage, text |
GPT-6 LunaOpenAI | vs | Grok 4.6xAI | cross-developer peersimage, text |
 Gemini 3.1 Flash-LiteGoogle DeepMind | vs | GPT-6 LunaOpenAI | cross-developer peerstext |
Gemini 3.7 FlashGoogle DeepMind | vs | GPT-6 LunaOpenAI | cross-developer peerstext |
GPT-6 LunaOpenAI | vs | GPT-6 SolOpenAI | family variantsimage, text |
Primary Evidence
Sources and Freshness
Questions
GPT-6 Luna vs Qwen3.8 Flash FAQs
Is GPT-6 Luna or Qwen3.8 Flash better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both GPT-6 Luna and Qwen3.8 Flash, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, GPT-6 Luna or Qwen3.8 Flash?+
GPT-6 Luna is $0.10 and Qwen3.8 Flash is $0.113 per million tokens, so GPT-6 Luna is cheaper on this metric. GPT-6 Luna is $0.50 and Qwen3.8 Flash is $0.382 per million tokens, so Qwen3.8 Flash is cheaper on this metric.
Which has a larger context window, GPT-6 Luna or Qwen3.8 Flash?+
GPT-6 Luna has the larger sourced context window. GPT-6 Luna supports 1,050,000 tokens and Qwen3.8 Flash supports 1,000,000 tokens.
Which performs better in benchmarks, GPT-6 Luna or Qwen3.8 Flash?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can GPT-6 Luna or Qwen3.8 Flash be self-hosted?+
Both models have the same sourced self-hosting status: unsupported. GPT-6 Luna is not marked open weight; Qwen3.8 Flash is not marked open weight.
Can GPT-6 Luna and Qwen3.8 Flash understand images?+
GPT-6 Luna is documented with image input; Qwen3.8 Flash is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, GPT-6 Luna or Qwen3.8 Flash?+
Qwen3.8 Flash has the larger sourced maximum output. GPT-6 Luna supports 128,000 tokens and Qwen3.8 Flash supports 131,072 tokens.
Do GPT-6 Luna and Qwen3.8 Flash support reasoning and tool use?+
GPT-6 Luna: reasoning, tool calling, and image input. Qwen3.8 Flash: reasoning, tool calling, and image input. Feature support does not establish relative quality.
Which is available from more inference providers, GPT-6 Luna or Qwen3.8 Flash?+
GPT-6 Luna has 2 sourced provider routes; Qwen3.8 Flash has 4, so Qwen3.8 Flash has broader tracked availability.
Which offers better value, GPT-6 Luna or Qwen3.8 Flash?+
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.