DeepSeek V4.1 Flash 1017 vs GPT-6.1 Terra
Too close to call on our weighted score (DeepSeek V4.1 Flash 1017 79, GPT-6.1 Terra 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-6.1 Terra
76/100- ECI168.1
- Price$1.00 / $5.00
- 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 3 points (DeepSeek V4.1 Flash 1017 79/100, GPT-6.1 Terra 76/100), so choose by what matters most for your work: GPT-6.1 Terra for raw capability and DeepSeek V4.1 Flash 1017 on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityGPT-6.1 TerraCapabilities Index (ECI): GPT-6.1 Terra 168.1 · DeepSeek V4.1 Flash 1017 158.8
- Lowest priceDeepSeek V4.1 Flash 1017DeepSeek V4.1 Flash 1017 $0.263 · GPT-6.1 Terra $2.00 per 1M tokens (3:1 blend)
- Longest contextGPT-6.1 TerraGPT-6.1 Terra 1,050,000 · DeepSeek V4.1 Flash 1017 1,000,000 tokens
- Widest inputsGPT-6.1 TerraDeepSeek V4.1 Flash 1017: Text, Images · GPT-6.1 Terra: Text, Images, PDFs
- Self-hostingDeepSeek V4.1 Flash 1017Publishes downloadable weights (MIT)
| Measure | Weight | DeepSeek V4.1 Flash 1017 | GPT-6.1 Terra |
|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 87 | 99 |
| Price | 25% | 74 | 34 |
| 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-6.1 TerraOpenAI |
|---|---|---|
| Capability | ||
| Capabilities Index (ECI) | 158.8 | 168.1 (best) |
| ECI rank | #19 of 165 | #3 of 165 (best) |
| GPQA DiamondGraduate-level science questions | 93.2% | 94.3% (best) |
| FrontierMath Tiers 1–3Research-level mathematics | 71.2% | 90.9% (best) |
| OTIS Mock AIME 2024–2025Competition mathematics | 99.4% | 100% (best) |
| SimpleQA VerifiedShort factual questions | 42.5% | 57.3% (best) |
| Price per million tokens | ||
| Input | $0.15 (best) | $1.00 |
| Output | $0.60 (best) | $5.00 |
| Cached input | $0.003 (best) | $0.05 |
| Blended (3:1) | $0.263 (best) | $2.00 |
| Long-context rate | Same rate | Over 272K: $2.00 / $7.50 |
| 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-6.1-terra |
| API providers | 40 (best) | 22 |
| Released | Oct 17, 2026 | Oct 26, 2026 |
| Knowledge cutoff | May 2025 | Jun 1, 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-6.1 Terra$20.00
Which should you choose?
Which is better: DeepSeek V4.1 Flash 1017 or GPT-6.1 Terra?
It is close. Our weighted score puts them within 3 points (DeepSeek V4.1 Flash 1017 79/100, GPT-6.1 Terra 76/100), so choose by what matters most for your work: GPT-6.1 Terra for raw capability and DeepSeek V4.1 Flash 1017 on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, DeepSeek V4.1 Flash 1017 or GPT-6.1 Terra?
DeepSeek V4.1 Flash 1017 is cheaper at $0.15 input / $0.60 output per million tokens (official DeepSeek API price). GPT-6.1 Terra costs $1.00 input / $5.00 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 $2.00 for GPT-6.1 Terra (7.6× as much).
Which scores higher on benchmarks?
GPT-6.1 Terra scores higher on the Capabilities Index (ECI): GPT-6.1 Terra 168.1 (#3 of 165) and DeepSeek V4.1 Flash 1017 158.8 (#19 of 165). Their confidence ranges do not overlap (164.9–172.9 vs 156.7–162.2), so the gap is a real one. On individual benchmarks: GPQA Diamond — GPT-6.1 Terra 94.3%, DeepSeek V4.1 Flash 1017 93.2%; FrontierMath Tiers 1–3 — GPT-6.1 Terra 90.9%, DeepSeek V4.1 Flash 1017 71.2%; OTIS Mock AIME 2024–2025 — GPT-6.1 Terra 100%, DeepSeek V4.1 Flash 1017 99.4%; SimpleQA Verified — GPT-6.1 Terra 57.3%, DeepSeek V4.1 Flash 1017 42.5%.
Which is better for coding?
There are no published SWE-bench Verified results for DeepSeek V4.1 Flash 1017 and GPT-6.1 Terra yet, so there is no like-for-like coding score. On overall capability, GPT-6.1 Terra 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-6.1 Terra 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-6.1 Terra up to 128,000 tokens.
Which can read images, PDFs, audio or video?
DeepSeek V4.1 Flash 1017 accepts text and images; GPT-6.1 Terra accepts text, images and PDFs. GPT-6.1 Terra 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-6.1 Terra is proprietary.
Which is newer?
GPT-6.1 Terra is the newest, released Oct 26, 2026. DeepSeek V4.1 Flash 1017 came out Oct 17, 2026. Knowledge cutoff: DeepSeek V4.1 Flash 1017 May 2025, GPT-6.1 Terra Jun 1, 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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