Gemini 2.0 Flash vs DeepSeek-V3.1 vs Claude Sonnet 3.5 v2
Gemini 2.0 Flash comes out ahead, 65 to 54 and 54 on our weighted score.
- Our pick
GoogleGemini 2.0 Flash
65/100- ECI134.7
- Price—
- Context1.05M
DeepSeekDeepSeek-V3.1
54/100- ECI139.9
- Price$0.385 / $1.25
- Context131K
AnthropicClaude Sonnet 3.5 v2
54/100- ECI133.5
- Price$3.00 / $15.00
- Context200K
Gemini 2.0 Flash is our pick
Gemini 2.0 Flash is the better all-round choice, scoring 65/100 against Claude Sonnet 3.5 v2 (54) and DeepSeek-V3.1 (54). It leads on inputs & features and context window. DeepSeek-V3.1 wins on capability. The score weighs capability 67%, inputs & features 20%, context window 13%.
- CapabilityDeepSeek-V3.1Capabilities Index (ECI): DeepSeek-V3.1 139.9 · Gemini 2.0 Flash 134.7 · Claude Sonnet 3.5 v2 133.5
- Lowest priceDeepSeek-V3.1DeepSeek-V3.1 $0.601 · Claude Sonnet 3.5 v2 $6.00 per 1M tokens (3:1 blend) · Gemini 2.0 Flash unpriced
- Longest contextGemini 2.0 FlashGemini 2.0 Flash 1,048,576 · Claude Sonnet 3.5 v2 200,000 · DeepSeek-V3.1 131,072 tokens
- Widest inputsGemini 2.0 FlashGemini 2.0 Flash: Text, Images, PDFs, Audio, Video · DeepSeek-V3.1: Text · Claude Sonnet 3.5 v2: Text, Images, PDFs
- Self-hostingDeepSeek-V3.1Publishes downloadable weights (MIT License)
| Measure | Weight | Gemini 2.0 Flash | DeepSeek-V3.1 | Claude Sonnet 3.5 v2 |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 67% | 59 | 65 | 57 |
| Inputs & features | 20% | 90 | 35 | 60 |
| Context window | 13% | 61 | 24 | 32 |
| Overall | 100% | 65/100 | 54/100 | 54/100 |
Left out because at least one model lacks the data: price. The remaining weights were rescaled.
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Gemini 2.0 FlashGoogle | DeepSeek-V3.1DeepSeek | Claude Sonnet 3.5 v2Anthropic |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 134.7 | 139.9 (best) | 133.5 |
| ECI rank | #116 of 148 | #100 of 148 (best) | #119 of 148 |
| GPQA DiamondGraduate-level science questions | — | — | 55.3% |
| OTIS Mock AIME 2024–2025Competition mathematics | — | — | 8.5% |
| Price per million tokens | |||
| Input | — | $0.385 (best) | $3.00 |
| Output | — | $1.25 (best) | $15.00 |
| Cached input | — | — | — |
| Blended (3:1) | — | $0.601 (best) | $6.00 |
| Long-context rate | — | Same rate | Same rate |
| Price source | — | Median of 8 providers | Median of 1 providers |
| Limits | |||
| Context window | 1,048,576 tokens (best) | 131,072 tokens | 200,000 tokens |
| Max output | 8,192 tokens | 8,192 tokens | 8,192 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | No | Yes |
| PDFs | Yes | No | Yes |
| Audio | Yes | No | No |
| Video | Yes | No | No |
| Reasoning | No | Yes | No |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | No | No |
| Availability | |||
| Weights | Proprietary | OpenMIT License | Proprietary |
| API model ID | — | — | — |
| API providers | — | 8 (best) | 1 |
| Released | Dec 11, 2024 | Aug 21, 2025 | Oct 22, 2024 |
| Knowledge cutoff | Jun 2024 | — | Apr 30, 2024 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Gemini 2.0 Flash—
- DeepSeek-V3.1$6.35
- Claude Sonnet 3.5 v2$60.00
Which should you choose?
Which is better: Gemini 2.0 Flash, DeepSeek-V3.1 or Claude Sonnet 3.5 v2?
Gemini 2.0 Flash is the better all-round choice, scoring 65/100 against Claude Sonnet 3.5 v2 (54) and DeepSeek-V3.1 (54). It leads on inputs & features and context window. DeepSeek-V3.1 wins on capability. The score weighs capability 67%, inputs & features 20%, context window 13%.
Which is cheaper, Gemini 2.0 Flash, DeepSeek-V3.1 or Claude Sonnet 3.5 v2?
DeepSeek-V3.1 is cheaper at $0.385 input / $1.25 output per million tokens (median across 8 API providers). Claude Sonnet 3.5 v2 costs $3.00 input / $15.00 output per million tokens (median across 1 API provider). At a typical mix of three input tokens to one output token, that is $0.601 per million tokens for DeepSeek-V3.1 versus $6.00 for Claude Sonnet 3.5 v2 (10× as much). Gemini 2.0 Flash has no published per-token price.
Which scores higher on benchmarks?
DeepSeek-V3.1 scores higher on the Capabilities Index (ECI): DeepSeek-V3.1 139.9 (#100 of 148), Gemini 2.0 Flash 134.7 (#116 of 148) and Claude Sonnet 3.5 v2 133.5 (#119 of 148). The confidence ranges of the top two overlap (136.1–143.3 vs 124.3–136.7), so treat the gap as small.
Which is better for coding?
There are no published SWE-bench Verified results for Gemini 2.0 Flash, DeepSeek-V3.1 and Claude Sonnet 3.5 v2 yet, so there is no like-for-like coding score. On overall capability, DeepSeek-V3.1 leads, which tends to carry over to coding, but test on your own codebase. All three support tool calling for agent workflows.
Which has the bigger context window?
Gemini 2.0 Flash has the largest context window at 1,048,576 tokens, against 200,000 for Claude Sonnet 3.5 v2 and 131,072 for DeepSeek-V3.1. Maximum output per response: Gemini 2.0 Flash up to 8,192, DeepSeek-V3.1 up to 8,192, Claude Sonnet 3.5 v2 up to 8,192 tokens.
Which can read images, PDFs, audio or video?
Gemini 2.0 Flash accepts text, images, PDFs, audio and video; DeepSeek-V3.1 accepts text; Claude Sonnet 3.5 v2 accepts text, images and PDFs. Gemini 2.0 Flash handles the widest range of inputs.
Are any of these open source?
DeepSeek-V3.1 publishes its weights (MIT License) and can be self-hosted; Gemini 2.0 Flash and Claude Sonnet 3.5 v2 is proprietary.
Which is newer?
DeepSeek-V3.1 is the newest, released Aug 21, 2025. Gemini 2.0 Flash came out Dec 11, 2024; Claude Sonnet 3.5 v2 came out Oct 22, 2024. Knowledge cutoff: Gemini 2.0 Flash Jun 2024, Claude Sonnet 3.5 v2 Apr 30, 2024.
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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