DeepSeek-R1 vs Gemini 2.5 Flash vs Kimi K2 Instruct
Gemini 2.5 Flash comes out ahead, 66 to 50 and 48 on our weighted score, and it is the cheaper option too.
DeepSeekDeepSeek-R1
48/100- ECI138.8
- Price$0.70 / $4.88
- Context128K
- Our pick
GoogleGemini 2.5 Flash
66/100- ECI140.6
- Price$0.30 / $2.50
- Context1.05M
Moonshot AIKimi K2 Instruct
50/100- ECI139.9
- Price$0.57 / $2.30
- Context131K
Gemini 2.5 Flash is our pick
Gemini 2.5 Flash is the better all-round choice, scoring 66/100 against Kimi K2 Instruct (50) and DeepSeek-R1 (48). It leads on price, inputs & features and context window. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityGemini 2.5 FlashCapabilities Index (ECI): Gemini 2.5 Flash 140.6 · Kimi K2 Instruct 139.9 · DeepSeek-R1 138.8
- Lowest priceGemini 2.5 FlashGemini 2.5 Flash $0.85 · Kimi K2 Instruct $1.00 · DeepSeek-R1 $1.75 per 1M tokens (3:1 blend)
- Longest contextGemini 2.5 FlashGemini 2.5 Flash 1,048,576 · Kimi K2 Instruct 131,072 · DeepSeek-R1 128,000 tokens
- Widest inputsGemini 2.5 FlashDeepSeek-R1: Text · Gemini 2.5 Flash: Text, Images, PDFs, Audio, Video · Kimi K2 Instruct: Text
- Self-hostingDeepSeek-R1 and Kimi K2 InstructPublishes downloadable weights (Modified MIT)
| Measure | Weight | DeepSeek-R1 | Gemini 2.5 Flash | Kimi K2 Instruct |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 63 | 65 | 64 |
| Price | 25% | 37 | 51 | 48 |
| Inputs & features | 15% | 35 | 100 | 25 |
| Context window | 10% | 24 | 61 | 25 |
| Overall | 100% | 48/100 | 66/100 | 50/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | DeepSeek-R1DeepSeek | Gemini 2.5 FlashGoogle | Kimi K2 InstructMoonshot AI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 138.8 | 140.6 (best) | 139.9 |
| ECI rank | #119 of 165 | #110 of 165 (best) | #112 of 165 |
| GPQA DiamondGraduate-level science questions | 71.7% | — | — |
| OTIS Mock AIME 2024–2025Competition mathematics | 53.3% | — | — |
| Price per million tokens | |||
| Input | $0.70 | $0.30 (best) | $0.57 |
| Output | $4.88 | $2.50 | $2.30 (best) |
| Cached input | — | $0.03 | — |
| Blended (3:1) | $1.75 | $0.85 (best) | $1.00 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 11 providers | Official Google API | Median of 11 providers |
| Limits | |||
| Context window | 128,000 tokens | 1,048,576 tokens (best) | 131,072 tokens |
| Max output | 32,768 tokens | 65,536 tokens (best) | 32,768 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | No |
| PDFs | No | Yes | No |
| Audio | No | Yes | No |
| Video | No | Yes | No |
| Reasoning | Yes | Yes | No |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | Yes | No |
| Availability | |||
| Weights | Open | Proprietary | OpenModified MIT |
| API model ID | — | gemini-2.5-flash | — |
| API providers | 12 | 32 (best) | 11 |
| Released | Jan 20, 2025 | Jun 17, 2025 | Jul 11, 2025 |
| Knowledge cutoff | Jul 2024 | Jan 2025 | — |
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$16.76
- Gemini 2.5 Flash$8.00
- Kimi K2 Instruct$10.30
Which should you choose?
Which is better: DeepSeek-R1, Gemini 2.5 Flash or Kimi K2 Instruct?
Gemini 2.5 Flash is the better all-round choice, scoring 66/100 against Kimi K2 Instruct (50) and DeepSeek-R1 (48). It leads on price, inputs & features and context window. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, DeepSeek-R1, Gemini 2.5 Flash or Kimi K2 Instruct?
Gemini 2.5 Flash is cheaper at $0.30 input / $2.50 output per million tokens (official Google API price). Kimi K2 Instruct costs $0.57 input / $2.30 output per million tokens (median across 11 API providers); DeepSeek-R1 costs $0.70 input / $4.88 output per million tokens (median across 11 API providers). At a typical mix of three input tokens to one output token, that is $0.85 per million tokens for Gemini 2.5 Flash versus $1.00 for Kimi K2 Instruct (1.2× as much) and $1.75 for DeepSeek-R1 (2.1× as much).
Which scores higher on benchmarks?
Gemini 2.5 Flash scores higher on the Capabilities Index (ECI): Gemini 2.5 Flash 140.6 (#110 of 165), Kimi K2 Instruct 139.9 (#112 of 165) and DeepSeek-R1 138.8 (#119 of 165). The confidence ranges of the top two overlap (138.4–142.1 vs 136.3–141.6), so treat the gap as small.
Which is better for coding?
There are no published SWE-bench Verified results for DeepSeek-R1, Gemini 2.5 Flash and Kimi K2 Instruct yet, so there is no like-for-like coding score. On overall capability, Gemini 2.5 Flash 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 2.5 Flash has the largest context window at 1,048,576 tokens, against 131,072 for Kimi K2 Instruct and 128,000 for DeepSeek-R1. Maximum output per response: DeepSeek-R1 up to 32,768, Gemini 2.5 Flash up to 65,536, Kimi K2 Instruct up to 32,768 tokens.
Which can read images, PDFs, audio or video?
DeepSeek-R1 accepts text; Gemini 2.5 Flash accepts text, images, PDFs, audio and video; Kimi K2 Instruct accepts text. Gemini 2.5 Flash handles the widest range of inputs.
Are any of these open source?
DeepSeek-R1 and Kimi K2 Instruct publishes its weights (Modified MIT) and can be self-hosted; Gemini 2.5 Flash is proprietary.
Which is newer?
Kimi K2 Instruct is the newest, released Jul 11, 2025. Gemini 2.5 Flash came out Jun 17, 2025; DeepSeek-R1 came out Jan 20, 2025. Knowledge cutoff: DeepSeek-R1 Jul 2024, Gemini 2.5 Flash Jan 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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