DeepSeek V4.1 Flash 1017 vs GPT-5.6 Luna
Too close to call on our weighted score (DeepSeek V4.1 Flash 1017 79, GPT-5.6 Luna 76). The right pick depends on what you value most.
DeepSeekDeepSeek V4.1 Flash 1017
79/100- ECI158.8
- Price$0.15 / $0.60
- Context1M
OpenAIGPT-5.6 Luna
76/100- ECI156.7
- Price$0.20 / $1.20
- Context1.05M
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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 (DeepSeek V4.1 Flash 1017 79/100, GPT-5.6 Luna 76/100), so choose by what matters most for your work: DeepSeek V4.1 Flash 1017 for raw capability and GPT-5.6 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-5.6 Luna 156.7
- Lowest priceDeepSeek V4.1 Flash 1017DeepSeek V4.1 Flash 1017 $0.263 · GPT-5.6 Luna $0.45 per 1M tokens (3:1 blend)
- Longest contextGPT-5.6 LunaGPT-5.6 Luna 1,050,000 · DeepSeek V4.1 Flash 1017 1,000,000 tokens
- Widest inputsGPT-5.6 LunaDeepSeek V4.1 Flash 1017: Text, Images · GPT-5.6 Luna: Text, Images, PDFs
- Self-hostingDeepSeek V4.1 Flash 1017Publishes downloadable weights (MIT)
| Measure | Weight | DeepSeek V4.1 Flash 1017 | GPT-5.6 Luna |
|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 87 | 85 |
| Price | 25% | 74 | 64 |
| Inputs & features | 15% | 70 | 80 |
| Context window | 10% | 60 | 61 |
| Overall | 100% | 79/100 | 76/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | DeepSeek V4.1 Flash 1017DeepSeek | GPT-5.6 LunaOpenAI |
|---|---|---|
| Capability | ||
| Capabilities Index (ECI) | 158.8 (best) | 156.7 |
| ECI rank | #19 of 165 (best) | #30 of 165 |
| GPQA DiamondGraduate-level science questions | 93.2% (best) | 91.6% |
| FrontierMath Tiers 1–3Research-level mathematics | 71.2% | 82.1% (best) |
| OTIS Mock AIME 2024–2025Competition mathematics | 99.4% (best) | 98.3% |
| SimpleQA VerifiedShort factual questions | 42.5% (best) | 41.0% |
| Price per million tokens | ||
| Input | $0.15 (best) | $0.20 |
| Output | $0.60 (best) | $1.20 |
| Cached input | $0.003 (best) | $0.02 |
| Blended (3:1) | $0.263 (best) | $0.45 |
| Long-context rate | Same rate | Over 272K: $0.40 / $1.80 |
| Price source | Official DeepSeek API | Official OpenAI API |
| Limits | ||
| Context window | 1,000,000 tokens | 1,050,000 tokens (best) |
| Max output | 384,000 tokens (best) | 128,000 tokens |
| Inputs and features | ||
| Text | Yes | Yes |
| Images | Yes | Yes |
| PDFs | No | Yes |
| Audio | No | No |
| Video | No | No |
| Reasoning | Yeslow · high · max | Yeslow · medium · high · xhigh · max |
| Tool calling | Yes | Yes |
| Structured output | Yes | Yes |
| Availability | ||
| Weights | OpenMIT | Proprietary |
| API model ID | deepseek-flash | gpt-5.6-luna |
| API providers | 40 | 47 (best) |
| Released | Oct 17, 2026 | Jul 9, 2026 |
| Knowledge cutoff | May 2025 | Feb 16, 2026 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- DeepSeek V4.1 Flash 1017$2.70
- GPT-5.6 Luna$4.40
Which should you choose?
Which is better: DeepSeek V4.1 Flash 1017 or GPT-5.6 Luna?
It is close. Our weighted score puts them within 2 points (DeepSeek V4.1 Flash 1017 79/100, GPT-5.6 Luna 76/100), so choose by what matters most for your work: DeepSeek V4.1 Flash 1017 for raw capability and GPT-5.6 Luna for long inputs. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, DeepSeek V4.1 Flash 1017 or GPT-5.6 Luna?
DeepSeek V4.1 Flash 1017 is cheaper at $0.15 input / $0.60 output per million tokens (official DeepSeek API price). GPT-5.6 Luna costs $0.20 input / $1.20 output per million tokens (official OpenAI API price). At a typical mix of three input tokens to one output token, that is $0.263 per million tokens for DeepSeek V4.1 Flash 1017 versus $0.45 for GPT-5.6 Luna (1.7× 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-5.6 Luna 156.7 (#30 of 165). The confidence ranges of the top two overlap (156.7–162.2 vs 154.2–159.3), so treat the gap as small. On individual benchmarks: GPQA Diamond — DeepSeek V4.1 Flash 1017 93.2%, GPT-5.6 Luna 91.6%; FrontierMath Tiers 1–3 — GPT-5.6 Luna 82.1%, DeepSeek V4.1 Flash 1017 71.2%; OTIS Mock AIME 2024–2025 — DeepSeek V4.1 Flash 1017 99.4%, GPT-5.6 Luna 98.3%; SimpleQA Verified — DeepSeek V4.1 Flash 1017 42.5%, GPT-5.6 Luna 41.0%.
Which is better for coding?
There are no published SWE-bench Verified results for DeepSeek V4.1 Flash 1017 and GPT-5.6 Luna 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-5.6 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: DeepSeek V4.1 Flash 1017 up to 384,000, GPT-5.6 Luna up to 128,000 tokens.
Which can read images, PDFs, audio or video?
DeepSeek V4.1 Flash 1017 accepts text and images; GPT-5.6 Luna accepts text, images and PDFs. GPT-5.6 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-5.6 Luna is proprietary.
Which is newer?
DeepSeek V4.1 Flash 1017 is the newest, released Oct 17, 2026. GPT-5.6 Luna came out Jul 9, 2026. Knowledge cutoff: DeepSeek V4.1 Flash 1017 May 2025, GPT-5.6 Luna Feb 16, 2026.
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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