Gemini 3.7 Flash vs DeepSeek V4.1 Flash 1017
DeepSeek V4.1 Flash 1017 comes out ahead, 79 to 74 on our weighted score, and it is the cheaper option too.
GoogleGemini 3.7 Flash
74/100- ECI158.2
- Price$0.75 / $3.75
- Context1.05M
- Our pick
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.
DeepSeek V4.1 Flash 1017 is our pick
DeepSeek V4.1 Flash 1017 is the better all-round choice, scoring 79/100 against Gemini 3.7 Flash (74). It leads on price. Gemini 3.7 Flash wins on inputs & features. 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 · Gemini 3.7 Flash 158.2
- Lowest priceDeepSeek V4.1 Flash 1017DeepSeek V4.1 Flash 1017 $0.263 · Gemini 3.7 Flash $1.50 per 1M tokens (3:1 blend)
- Longest contextGemini 3.7 FlashGemini 3.7 Flash 1,048,576 · DeepSeek V4.1 Flash 1017 1,000,000 tokens
- Widest inputsGemini 3.7 FlashGemini 3.7 Flash: Text, Images, PDFs, Audio, Video · DeepSeek V4.1 Flash 1017: Text, Images
- Self-hostingDeepSeek V4.1 Flash 1017Publishes downloadable weights (MIT)
| Measure | Weight | Gemini 3.7 Flash | DeepSeek V4.1 Flash 1017 |
|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 87 | 87 |
| Price | 25% | 40 | 74 |
| Inputs & features | 15% | 100 | 70 |
| Context window | 10% | 61 | 60 |
| Overall | 100% | 74/100 | 79/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Gemini 3.7 FlashGoogle | DeepSeek V4.1 Flash 1017DeepSeek |
|---|---|---|
| Capability | ||
| Capabilities Index (ECI) | 158.2 | 158.8 (best) |
| ECI rank | #21 of 165 | #19 of 165 (best) |
| GPQA DiamondGraduate-level science questions | 94.8% (best) | 93.2% |
| FrontierMath Tiers 1–3Research-level mathematics | 71.6% (best) | 71.2% |
| OTIS Mock AIME 2024–2025Competition mathematics | 97.2% | 99.4% (best) |
| SimpleQA VerifiedShort factual questions | 69.2% (best) | 42.5% |
| Price per million tokens | ||
| Input | $0.75 | $0.15 (best) |
| Output | $3.75 | $0.60 (best) |
| Cached input | $0.075 | $0.003 (best) |
| Blended (3:1) | $1.50 | $0.263 (best) |
| Long-context rate | Same rate | Same rate |
| Price source | Official Google API | Official DeepSeek API |
| Limits | ||
| Context window | 1,048,576 tokens (best) | 1,000,000 tokens |
| Max output | 65,536 tokens | 384,000 tokens (best) |
| Inputs and features | ||
| Text | Yes | Yes |
| Images | Yes | Yes |
| PDFs | Yes | No |
| Audio | Yes | No |
| Video | Yes | No |
| Reasoning | Yeslow · medium · high | Yeslow · high · max |
| Tool calling | Yes | Yes |
| Structured output | Yes | Yes |
| Availability | ||
| Weights | Proprietary | OpenMIT |
| API model ID | gemini-3.7-flash | deepseek-flash |
| API providers | 31 | 40 (best) |
| Released | Aug 13, 2026 | Oct 17, 2026 |
| Knowledge cutoff | Mar 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.
- Gemini 3.7 Flash$15.00
- DeepSeek V4.1 Flash 1017$2.70
Which should you choose?
Which is better: Gemini 3.7 Flash or DeepSeek V4.1 Flash 1017?
DeepSeek V4.1 Flash 1017 is the better all-round choice, scoring 79/100 against Gemini 3.7 Flash (74). It leads on price. Gemini 3.7 Flash wins on inputs & features. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, Gemini 3.7 Flash or DeepSeek V4.1 Flash 1017?
DeepSeek V4.1 Flash 1017 is cheaper at $0.15 input / $0.60 output per million tokens (official DeepSeek API price). Gemini 3.7 Flash costs $0.75 input / $3.75 output per million tokens (official Google 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 $1.50 for Gemini 3.7 Flash (5.7× 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 Gemini 3.7 Flash 158.2 (#21 of 165). The confidence ranges of the top two overlap (156.7–162.2 vs 156.2–161.4), so treat the gap as small. On individual benchmarks: GPQA Diamond — Gemini 3.7 Flash 94.8%, DeepSeek V4.1 Flash 1017 93.2%; FrontierMath Tiers 1–3 — Gemini 3.7 Flash 71.6%, DeepSeek V4.1 Flash 1017 71.2%; OTIS Mock AIME 2024–2025 — DeepSeek V4.1 Flash 1017 99.4%, Gemini 3.7 Flash 97.2%; SimpleQA Verified — Gemini 3.7 Flash 69.2%, DeepSeek V4.1 Flash 1017 42.5%.
Which is better for coding?
There are no published SWE-bench Verified results for Gemini 3.7 Flash 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?
Gemini 3.7 Flash has the largest context window at 1,048,576 tokens, against 1,000,000 for DeepSeek V4.1 Flash 1017. Maximum output per response: Gemini 3.7 Flash up to 65,536, DeepSeek V4.1 Flash 1017 up to 384,000 tokens.
Which can read images, PDFs, audio or video?
Gemini 3.7 Flash accepts text, images, PDFs, audio and video; DeepSeek V4.1 Flash 1017 accepts text and images. Gemini 3.7 Flash 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; Gemini 3.7 Flash is proprietary.
Which is newer?
DeepSeek V4.1 Flash 1017 is the newest, released Oct 17, 2026. Gemini 3.7 Flash came out Aug 13, 2026. Knowledge cutoff: Gemini 3.7 Flash Mar 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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