DeepSeek-R1 vs DeepSeek-V3.1
DeepSeek-V3.1 comes out ahead, 55 to 51 on our weighted score, and it is the cheaper option too.
DeepSeekDeepSeek-R1
51/100- ECI139.0
- Price$0.70 / $2.60
- Context128K
- Our pickDeepSeek
DeepSeek-V3.1
55/100- ECI139.9
- Price$0.385 / $1.25
- Context131K
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Make it a three-way comparison.
DeepSeek-V3.1 is our pick
DeepSeek-V3.1 is the better all-round choice, scoring 55/100 against DeepSeek-R1 (51). It leads on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityDeepSeek-V3.1Capabilities Index (ECI): DeepSeek-V3.1 139.9 · DeepSeek-R1 139.0
- Lowest priceDeepSeek-V3.1DeepSeek-V3.1 $0.601 · DeepSeek-R1 $1.18 per 1M tokens (3:1 blend)
- Longest contextDeepSeek-V3.1DeepSeek-V3.1 131,072 · DeepSeek-R1 128,000 tokens
- Widest inputsSame inputsDeepSeek-R1: Text · DeepSeek-V3.1: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | DeepSeek-R1 | DeepSeek-V3.1 |
|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 64 | 65 |
| Price | 25% | 47 | 60 |
| Inputs & features | 15% | 35 | 35 |
| Context window | 10% | 24 | 24 |
| Overall | 100% | 51/100 | 55/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | DeepSeek-R1DeepSeek | DeepSeek-V3.1DeepSeek |
|---|---|---|
| Capability | ||
| Capabilities Index (ECI) | 139.0 | 139.9 (best) |
| ECI rank | #104 of 148 | #100 of 148 (best) |
| GPQA DiamondGraduate-level science questions | 71.7% | — |
| OTIS Mock AIME 2024–2025Competition mathematics | 53.3% | — |
| Price per million tokens | ||
| Input | $0.70 | $0.385 (best) |
| Output | $2.60 | $1.25 (best) |
| Cached input | — | — |
| Blended (3:1) | $1.18 | $0.601 (best) |
| Long-context rate | Same rate | Same rate |
| Price source | Median of 11 providers | Median of 8 providers |
| Limits | ||
| Context window | 128,000 tokens | 131,072 tokens (best) |
| Max output | 32,768 tokens (best) | 8,192 tokens |
| Inputs and features | ||
| Text | Yes | Yes |
| Images | No | No |
| PDFs | No | No |
| Audio | No | No |
| Video | No | No |
| Reasoning | Yes | Yes |
| Tool calling | Yes | Yes |
| Structured output | No | No |
| Availability | ||
| Weights | Open | OpenMIT License |
| API model ID | — | — |
| API providers | 12 (best) | 8 |
| Released | Jan 20, 2025 | Aug 21, 2025 |
| Knowledge cutoff | Jul 2024 | — |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- DeepSeek-R1$12.20
- DeepSeek-V3.1$6.35
Which should you choose?
Which is better: DeepSeek-R1 or DeepSeek-V3.1?
DeepSeek-V3.1 is the better all-round choice, scoring 55/100 against DeepSeek-R1 (51). It leads on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, DeepSeek-R1 or DeepSeek-V3.1?
DeepSeek-V3.1 is cheaper at $0.385 input / $1.25 output per million tokens (median across 8 API providers). DeepSeek-R1 costs $0.70 input / $2.60 output per million tokens (median across 11 API providers). At a typical mix of three input tokens to one output token, that is $0.601 per million tokens for DeepSeek-V3.1 versus $1.18 for DeepSeek-R1 (2× as much).
Which scores higher on benchmarks?
DeepSeek-V3.1 scores higher on the Capabilities Index (ECI): DeepSeek-V3.1 139.9 (#100 of 148) and DeepSeek-R1 139.0 (#104 of 148). The confidence ranges of the top two overlap (136.1–143.3 vs 136.2–140.4), so treat the gap as small.
Which is better for coding?
There are no published SWE-bench Verified results for DeepSeek-R1 and DeepSeek-V3.1 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. Both support tool calling for agent workflows.
Which has the bigger context window?
DeepSeek-V3.1 has the largest context window at 131,072 tokens, against 128,000 for DeepSeek-R1. Maximum output per response: DeepSeek-R1 up to 32,768, DeepSeek-V3.1 up to 8,192 tokens.
Which can read images, PDFs, audio or video?
DeepSeek-R1 accepts text; DeepSeek-V3.1 accepts text. They handle the same number of input types.
Are any of these open source?
Yes, both publish their weights (MIT License), so you can self-host them.
Which is newer?
DeepSeek-V3.1 is the newest, released Aug 21, 2025. DeepSeek-R1 came out Jan 20, 2025. Knowledge cutoff: DeepSeek-R1 Jul 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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