GPT-6 Luna vs DeepSeek V4 Flash 0731
GPT-6 Luna comes out ahead, 78 to 69 on our weighted score, though DeepSeek V4 Flash 0731 is 13% cheaper per token.
- Our pick
OpenAIGPT-6 Luna
78/100- ECI—
- Price$0.10 / $0.50
- Context1.05M
DeepSeekDeepSeek V4 Flash 0731
69/100- ECI154.5
- Price$0.14 / $0.28
- Context1M
Add a model
Make it a three-way comparison.
GPT-6 Luna is our pick
GPT-6 Luna is the better all-round choice, scoring 78/100 against DeepSeek V4 Flash 0731 (69). It leads on capability and inputs & features. DeepSeek V4 Flash 0731 wins on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%. Capability uses the average of shared benchmarks (GPQA Diamond, FrontierMath Tiers 1–3, OTIS Mock AIME 2024–2025 and SimpleQA Verified), because GPT-6 Luna has no Capabilities Index score yet.
- CapabilityGPT-6 LunaShared benchmarks: GPT-6 Luna 77.4% · DeepSeek V4 Flash 0731 69.2%
- Lowest priceDeepSeek V4 Flash 0731DeepSeek V4 Flash 0731 $0.175 · GPT-6 Luna $0.20 per 1M tokens (3:1 blend)
- Longest contextGPT-6 LunaGPT-6 Luna 1,050,000 · DeepSeek V4 Flash 0731 1,000,000 tokens
- Widest inputsGPT-6 LunaGPT-6 Luna: Text, Images, PDFs · DeepSeek V4 Flash 0731: Text
- Self-hostingDeepSeek V4 Flash 0731Publishes downloadable weights (MIT)
| Measure | Weight | GPT-6 Luna | DeepSeek V4 Flash 0731 |
|---|---|---|---|
| CapabilityShared benchmarks | 50% | 77 | 69 |
| Price | 25% | 83 | 86 |
| Inputs & features | 15% | 80 | 45 |
| Context window | 10% | 61 | 60 |
| Overall | 100% | 78/100 | 69/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | GPT-6 LunaOpenAI | DeepSeek V4 Flash 0731DeepSeek |
|---|---|---|
| Capability | ||
| Capabilities Index (ECI) | — | 154.5 |
| ECI rank | — | #32 of 148 |
| GPQA DiamondGraduate-level science questions | 90.5% | 91.0% (best) |
| FrontierMath Tiers 1–3Research-level mathematics | 79.0% (best) | 57.5% |
| OTIS Mock AIME 2024–2025Competition mathematics | 98.9% (best) | 94.4% |
| SimpleQA VerifiedShort factual questions | 41.4% (best) | 33.6% |
| Price per million tokens | ||
| Input | $0.10 (best) | $0.14 |
| Output | $0.50 | $0.28 (best) |
| Cached input | $0.01 | — |
| Blended (3:1) | $0.20 | $0.175 (best) |
| Long-context rate | Over 272K: $0.20 / $0.75 | Same rate |
| Price source | Official OpenAI API | Median of 48 providers |
| Limits | ||
| Context window | 1,050,000 tokens (best) | 1,000,000 tokens |
| Max output | 128,000 tokens | 384,000 tokens (best) |
| Inputs and features | ||
| Text | Yes | Yes |
| Images | Yes | No |
| PDFs | Yes | No |
| Audio | No | No |
| Video | No | No |
| Reasoning | Yeslow · medium · high · xhigh · max | Yes |
| Tool calling | Yes | Yes |
| Structured output | Yes | Yes |
| Availability | ||
| Weights | Proprietary | OpenMIT |
| API model ID | gpt-6-luna | — |
| API providers | 24 | 49 (best) |
| Released | Sep 22, 2026 | Jul 31, 2026 |
| Knowledge cutoff | May 18, 2026 | May 2025 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- GPT-6 Luna$2.00
- DeepSeek V4 Flash 0731$1.96
Which should you choose?
Which is better: GPT-6 Luna or DeepSeek V4 Flash 0731?
GPT-6 Luna is the better all-round choice, scoring 78/100 against DeepSeek V4 Flash 0731 (69). It leads on capability and inputs & features. DeepSeek V4 Flash 0731 wins on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%. Capability uses the average of shared benchmarks (GPQA Diamond, FrontierMath Tiers 1–3, OTIS Mock AIME 2024–2025 and SimpleQA Verified), because GPT-6 Luna has no Capabilities Index score yet.
Which is cheaper, GPT-6 Luna or DeepSeek V4 Flash 0731?
DeepSeek V4 Flash 0731 is cheaper at $0.14 input / $0.28 output per million tokens (median across 48 API providers). GPT-6 Luna costs $0.10 input / $0.50 output per million tokens (official OpenAI API price). At a typical mix of three input tokens to one output token, that is $0.175 per million tokens for DeepSeek V4 Flash 0731 versus $0.20 for GPT-6 Luna (1.1× as much).
Which scores higher on benchmarks?
Not every model here has a Capabilities Index score, so we compare the average of shared benchmarks (GPQA Diamond, FrontierMath Tiers 1–3, OTIS Mock AIME 2024–2025 and SimpleQA Verified): GPT-6 Luna 77.4% and DeepSeek V4 Flash 0731 69.2%. On individual benchmarks: GPQA Diamond — DeepSeek V4 Flash 0731 91.0%, GPT-6 Luna 90.5%; FrontierMath Tiers 1–3 — GPT-6 Luna 79.0%, DeepSeek V4 Flash 0731 57.5%; OTIS Mock AIME 2024–2025 — GPT-6 Luna 98.9%, DeepSeek V4 Flash 0731 94.4%; SimpleQA Verified — GPT-6 Luna 41.4%, DeepSeek V4 Flash 0731 33.6%.
Which is better for coding?
There are no published SWE-bench Verified results for GPT-6 Luna and DeepSeek V4 Flash 0731 yet, so there is no like-for-like coding score. On overall capability, GPT-6 Luna leads, which tends to carry over to coding, but test on your own codebase. Both support tool calling for agent workflows.
Which has the bigger context window?
GPT-6 Luna has the largest context window at 1,050,000 tokens, against 1,000,000 for DeepSeek V4 Flash 0731. Maximum output per response: GPT-6 Luna up to 128,000, DeepSeek V4 Flash 0731 up to 384,000 tokens.
Which can read images, PDFs, audio or video?
GPT-6 Luna accepts text, images and PDFs; DeepSeek V4 Flash 0731 accepts text. GPT-6 Luna handles the widest range of inputs.
Are any of these open source?
DeepSeek V4 Flash 0731 publishes its weights (MIT) and can be self-hosted; GPT-6 Luna is proprietary.
Which is newer?
GPT-6 Luna is the newest, released Sep 22, 2026. DeepSeek V4 Flash 0731 came out Jul 31, 2026. Knowledge cutoff: GPT-6 Luna May 18, 2026, DeepSeek V4 Flash 0731 May 2025.
How do you decide the winner?
Each model gets a 0–100 score on capability (50%, independent benchmark results); price (25%, blended price per million tokens (3 input : 1 output), log scale); inputs & features (15%, image, PDF, audio and video input, tool calling, structured output and reasoning); context window (10%, maximum tokens per request, log scale). Dimensions missing for any model are dropped and the remaining weights rescaled, so every model is judged on the same evidence. Specs and prices come from public model listings and the labs’ own API pages; capability scores come from independent benchmark runs. Data updated Oct 4, 2026.
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