GPT-6 Luna vs DeepSeek V4.1 Flash 1017
Too close to call on our weighted score (GPT-6 Luna 80, DeepSeek V4.1 Flash 1017 79). The right pick depends on what you value most.
OpenAIGPT-6 Luna
80/100- ECI156.8
- Price$0.10 / $0.50
- Context1.05M
DeepSeekDeepSeek V4.1 Flash 1017
79/100- ECI158.8
- Price$0.15 / $0.60
- Context1M
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Make it a three-way comparison.
Too close to call
It is close. Our weighted score puts them within 2 points (GPT-6 Luna 80/100, DeepSeek V4.1 Flash 1017 79/100), so choose by what matters most for your work: DeepSeek V4.1 Flash 1017 for raw capability, GPT-6 Luna on price and GPT-6 Luna for long inputs. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityDeepSeek V4.1 Flash 1017Capabilities Index (ECI): DeepSeek V4.1 Flash 1017 158.8 · GPT-6 Luna 156.8
- Lowest priceGPT-6 LunaGPT-6 Luna $0.20 · DeepSeek V4.1 Flash 1017 $0.263 per 1M tokens (3:1 blend)
- Longest contextGPT-6 LunaGPT-6 Luna 1,050,000 · DeepSeek V4.1 Flash 1017 1,000,000 tokens
- Widest inputsGPT-6 LunaGPT-6 Luna: Text, Images, PDFs · DeepSeek V4.1 Flash 1017: Text, Images
- Self-hostingDeepSeek V4.1 Flash 1017Publishes downloadable weights (MIT)
| Measure | Weight | GPT-6 Luna | DeepSeek V4.1 Flash 1017 |
|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 85 | 87 |
| Price | 25% | 80 | 74 |
| Inputs & features | 15% | 80 | 70 |
| Context window | 10% | 61 | 60 |
| Overall | 100% | 80/100 | 79/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | GPT-6 LunaOpenAI | DeepSeek V4.1 Flash 1017DeepSeek |
|---|---|---|
| Capability | ||
| Capabilities Index (ECI) | 156.8 | 158.8 (best) |
| ECI rank | #28 of 165 | #19 of 165 (best) |
| GPQA DiamondGraduate-level science questions | 90.5% | 93.2% (best) |
| FrontierMath Tiers 1–3Research-level mathematics | 79.0% (best) | 71.2% |
| OTIS Mock AIME 2024–2025Competition mathematics | 98.9% | 99.4% (best) |
| SimpleQA VerifiedShort factual questions | 41.4% | 42.5% (best) |
| Price per million tokens | ||
| Input | $0.10 (best) | $0.15 |
| Output | $0.50 (best) | $0.60 |
| Cached input | $0.01 | $0.003 (best) |
| Blended (3:1) | $0.20 (best) | $0.263 |
| Long-context rate | Over 272K: $0.20 / $0.75 | Same rate |
| Price source | Official OpenAI API | Official DeepSeek API |
| 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 | Yes |
| PDFs | Yes | No |
| Audio | No | No |
| Video | No | No |
| Reasoning | Yeslow · medium · high · xhigh · max | Yeslow · high · max |
| Tool calling | Yes | Yes |
| Structured output | Yes | Yes |
| Availability | ||
| Weights | Proprietary | OpenMIT |
| API model ID | gpt-6-luna | deepseek-flash |
| API providers | 35 | 40 (best) |
| Released | Sep 22, 2026 | Oct 17, 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 V4.1 Flash 1017$2.70
Which should you choose?
Which is better: GPT-6 Luna or DeepSeek V4.1 Flash 1017?
It is close. Our weighted score puts them within 2 points (GPT-6 Luna 80/100, DeepSeek V4.1 Flash 1017 79/100), so choose by what matters most for your work: DeepSeek V4.1 Flash 1017 for raw capability, GPT-6 Luna on price and GPT-6 Luna for long inputs. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, GPT-6 Luna or DeepSeek V4.1 Flash 1017?
GPT-6 Luna is cheaper at $0.10 input / $0.50 output per million tokens (official OpenAI API price). DeepSeek V4.1 Flash 1017 costs $0.15 input / $0.60 output per million tokens (official DeepSeek API price). At a typical mix of three input tokens to one output token, that is $0.20 per million tokens for GPT-6 Luna versus $0.263 for DeepSeek V4.1 Flash 1017 (1.3× as much).
Which scores higher on benchmarks?
DeepSeek V4.1 Flash 1017 scores higher on the Capabilities Index (ECI): DeepSeek V4.1 Flash 1017 158.8 (#19 of 165) and GPT-6 Luna 156.8 (#28 of 165). The confidence ranges of the top two overlap (156.7–162.2 vs 154.0–159.5), so treat the gap as small. On individual benchmarks: GPQA Diamond — DeepSeek V4.1 Flash 1017 93.2%, GPT-6 Luna 90.5%; FrontierMath Tiers 1–3 — GPT-6 Luna 79.0%, DeepSeek V4.1 Flash 1017 71.2%; OTIS Mock AIME 2024–2025 — DeepSeek V4.1 Flash 1017 99.4%, GPT-6 Luna 98.9%; SimpleQA Verified — DeepSeek V4.1 Flash 1017 42.5%, GPT-6 Luna 41.4%.
Which is better for coding?
There are no published SWE-bench Verified results for GPT-6 Luna and DeepSeek V4.1 Flash 1017 yet, so there is no like-for-like coding score. On overall capability, DeepSeek V4.1 Flash 1017 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 V4.1 Flash 1017. Maximum output per response: GPT-6 Luna up to 128,000, DeepSeek V4.1 Flash 1017 up to 384,000 tokens.
Which can read images, PDFs, audio or video?
GPT-6 Luna accepts text, images and PDFs; DeepSeek V4.1 Flash 1017 accepts text and images. GPT-6 Luna handles the widest range of inputs.
Are any of these open source?
DeepSeek V4.1 Flash 1017 publishes its weights (MIT) and can be self-hosted; GPT-6 Luna is proprietary.
Which is newer?
DeepSeek V4.1 Flash 1017 is the newest, released Oct 17, 2026. GPT-6 Luna came out Sep 22, 2026. Knowledge cutoff: GPT-6 Luna May 18, 2026, DeepSeek V4.1 Flash 1017 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 Nov 2, 2026.
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