Kimi K2 Instruct vs Baichuan M2 32B vs Gemini 2.5 Flash
Gemini 2.5 Flash comes out ahead, 68 to 58 and 36 on our weighted score, though Baichuan M2 32B is 12× cheaper per token.
Moonshot AIKimi K2 Instruct
36/100- ECI139.9
- Price$0.57 / $2.30
- Context131K
BaichuanBaichuan M2 32B
58/100- ECI—
- Price$0.07 / $0.07
- Context131K
- Our pick
GoogleGemini 2.5 Flash
68/100- ECI140.6
- Price$0.30 / $2.50
- Context1.05M
Gemini 2.5 Flash is our pick
Gemini 2.5 Flash is the better all-round choice, scoring 68/100 against Baichuan M2 32B (58) and Kimi K2 Instruct (36). It leads on inputs & features and context window. Baichuan M2 32B wins on price. The score weighs price 50%, inputs & features 30%, context window 20%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
- CapabilityNot enough dataNo independent benchmark covers every model here yet
- Lowest priceBaichuan M2 32BBaichuan M2 32B $0.07 · Gemini 2.5 Flash $0.85 · Kimi K2 Instruct $1.00 per 1M tokens (3:1 blend)
- Longest contextGemini 2.5 FlashGemini 2.5 Flash 1,048,576 · Kimi K2 Instruct 131,072 · Baichuan M2 32B 131,072 tokens
- Widest inputsGemini 2.5 FlashKimi K2 Instruct: Text · Baichuan M2 32B: Text · Gemini 2.5 Flash: Text, Images, PDFs, Audio, Video
- Self-hostingKimi K2 Instruct and Baichuan M2 32BPublishes downloadable weights (Modified MIT and Apache-2.0)
| Measure | Weight | Kimi K2 Instruct | Baichuan M2 32B | Gemini 2.5 Flash |
|---|---|---|---|---|
| Price | 50% | 48 | 100 | 51 |
| Inputs & features | 30% | 25 | 10 | 100 |
| Context window | 20% | 25 | 25 | 61 |
| Overall | 100% | 36/100 | 58/100 | 68/100 |
Left out because at least one model lacks the data: capability. The remaining weights were rescaled.
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Kimi K2 InstructMoonshot AI | Baichuan M2 32BBaichuan | Gemini 2.5 FlashGoogle |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 139.9 | — | 140.6 (best) |
| ECI rank | #112 of 165 | — | #110 of 165 (best) |
| Price per million tokens | |||
| Input | $0.57 | $0.07 (best) | $0.30 |
| Output | $2.30 | $0.07 (best) | $2.50 |
| Cached input | — | — | $0.03 |
| Blended (3:1) | $1.00 | $0.07 (best) | $0.85 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 11 providers | Median of 1 providers | Official Google API |
| Limits | |||
| Context window | 131,072 tokens | 131,072 tokens | 1,048,576 tokens (best) |
| Max output | 32,768 tokens | 131,072 tokens (best) | 65,536 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | No | Yes |
| PDFs | No | No | Yes |
| Audio | No | No | Yes |
| Video | No | No | Yes |
| Reasoning | No | Yes | Yes |
| Tool calling | Yes | No | Yes |
| Structured output | No | No | Yes |
| Availability | |||
| Weights | OpenModified MIT | OpenApache-2.0 | Proprietary |
| API model ID | — | — | gemini-2.5-flash |
| API providers | 11 | 1 | 32 (best) |
| Released | Jul 11, 2025 | Aug 13, 2025 | Jun 17, 2025 |
| Knowledge cutoff | — | — | 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.
- Kimi K2 Instruct$10.30
- Baichuan M2 32B$0.84
- Gemini 2.5 Flash$8.00
Which should you choose?
Which is better: Kimi K2 Instruct, Baichuan M2 32B or Gemini 2.5 Flash?
Gemini 2.5 Flash is the better all-round choice, scoring 68/100 against Baichuan M2 32B (58) and Kimi K2 Instruct (36). It leads on inputs & features and context window. Baichuan M2 32B wins on price. The score weighs price 50%, inputs & features 30%, context window 20%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
Which is cheaper, Kimi K2 Instruct, Baichuan M2 32B or Gemini 2.5 Flash?
Baichuan M2 32B is cheaper at $0.07 input / $0.07 output per million tokens (median across 1 API provider). Gemini 2.5 Flash costs $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). At a typical mix of three input tokens to one output token, that is $0.07 per million tokens for Baichuan M2 32B versus $0.85 for Gemini 2.5 Flash (12× as much) and $1.00 for Kimi K2 Instruct (14× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Kimi K2 Instruct has an ECI of 139.9, Baichuan M2 32B has not been scored yet and Gemini 2.5 Flash has an ECI of 140.6.
Which is better for coding?
There are no published SWE-bench Verified results for Kimi K2 Instruct, Baichuan M2 32B and Gemini 2.5 Flash yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Note that Baichuan M2 32B does not support tool calling, which most coding agents need.
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 131,072 for Baichuan M2 32B. Maximum output per response: Kimi K2 Instruct up to 32,768, Baichuan M2 32B up to 131,072, Gemini 2.5 Flash up to 65,536 tokens.
Which can read images, PDFs, audio or video?
Kimi K2 Instruct accepts text; Baichuan M2 32B accepts text; Gemini 2.5 Flash accepts text, images, PDFs, audio and video. Gemini 2.5 Flash handles the widest range of inputs.
Are any of these open source?
Kimi K2 Instruct and Baichuan M2 32B publishes its weights (Modified MIT and Apache-2.0) and can be self-hosted; Gemini 2.5 Flash is proprietary.
Which is newer?
Baichuan M2 32B is the newest, released Aug 13, 2025. Kimi K2 Instruct came out Jul 11, 2025; Gemini 2.5 Flash came out Jun 17, 2025. Knowledge cutoff: 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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