Baichuan M2 32B vs DeepSeek R1 0528 Qwen3 8B vs Kimi K2 Instruct
Too close to call on our weighted score (Baichuan M2 32B 58, DeepSeek R1 0528 Qwen3 8B 58, Kimi K2 Instruct 36). The right pick depends on what you value most.
BaichuanBaichuan M2 32B
58/100- ECI—
- Price$0.07 / $0.07
- Context131K
DeepSeekDeepSeek R1 0528 Qwen3 8B
58/100- ECI—
- Price$0.06 / $0.09
- Context131K
Moonshot AIKimi K2 Instruct
36/100- ECI139.9
- Price$0.57 / $2.30
- Context131K
Too close to call
It is close. Our weighted score puts them within a point (Baichuan M2 32B 58/100, DeepSeek R1 0528 Qwen3 8B 58/100, Kimi K2 Instruct 36/100), so choose by what matters most for your work: DeepSeek R1 0528 Qwen3 8B 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 priceDeepSeek R1 0528 Qwen3 8BDeepSeek R1 0528 Qwen3 8B $0.068 · Baichuan M2 32B $0.07 · Kimi K2 Instruct $1.00 per 1M tokens (3:1 blend)
- Longest contextAbout the sameBaichuan M2 32B 131,072 · DeepSeek R1 0528 Qwen3 8B 131,072 · Kimi K2 Instruct 131,072 tokens
- Widest inputsSame inputsBaichuan M2 32B: Text · DeepSeek R1 0528 Qwen3 8B: Text · Kimi K2 Instruct: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Baichuan M2 32B | DeepSeek R1 0528 Qwen3 8B | Kimi K2 Instruct |
|---|---|---|---|---|
| Price | 50% | 100 | 100 | 48 |
| Inputs & features | 30% | 10 | 10 | 25 |
| Context window | 20% | 25 | 25 | 25 |
| Overall | 100% | 58/100 | 58/100 | 36/100 |
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 | Baichuan M2 32BBaichuan | DeepSeek R1 0528 Qwen3 8BDeepSeek | Kimi K2 InstructMoonshot AI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | 139.9 |
| ECI rank | — | — | #112 of 165 |
| GPQA DiamondGraduate-level science questions | — | 9.3% | — |
| OTIS Mock AIME 2024–2025Competition mathematics | — | 43.9% | — |
| Price per million tokens | |||
| Input | $0.07 | $0.06 (best) | $0.57 |
| Output | $0.07 (best) | $0.09 | $2.30 |
| Cached input | — | — | — |
| Blended (3:1) | $0.07 | $0.068 (best) | $1.00 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 1 providers | Median of 1 providers | Median of 11 providers |
| Limits | |||
| Context window | 131,072 tokens | 131,072 tokens | 131,072 tokens |
| Max output | 131,072 tokens (best) | 32,000 tokens | 32,768 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | No | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | Yes | Yes | No |
| Tool calling | No | No | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | OpenApache-2.0 | OpenMIT | OpenModified MIT |
| API model ID | — | — | — |
| API providers | 1 | 1 | 11 (best) |
| Released | Aug 13, 2025 | May 29, 2025 | Jul 11, 2025 |
| Knowledge cutoff | — | — | — |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Baichuan M2 32B$0.84
- DeepSeek R1 0528 Qwen3 8B$0.78
- Kimi K2 Instruct$10.30
Which should you choose?
Which is better: Baichuan M2 32B, DeepSeek R1 0528 Qwen3 8B or Kimi K2 Instruct?
It is close. Our weighted score puts them within a point (Baichuan M2 32B 58/100, DeepSeek R1 0528 Qwen3 8B 58/100, Kimi K2 Instruct 36/100), so choose by what matters most for your work: DeepSeek R1 0528 Qwen3 8B 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, Baichuan M2 32B, DeepSeek R1 0528 Qwen3 8B or Kimi K2 Instruct?
DeepSeek R1 0528 Qwen3 8B is cheaper at $0.06 input / $0.09 output per million tokens (median across 1 API provider). Baichuan M2 32B costs $0.07 input / $0.07 output per million tokens (median across 1 API provider); 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 $0.068 per million tokens for DeepSeek R1 0528 Qwen3 8B versus $0.07 for Baichuan M2 32B (1× as much) and $1.00 for Kimi K2 Instruct (15× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Baichuan M2 32B has not been scored yet, DeepSeek R1 0528 Qwen3 8B has not been scored yet and Kimi K2 Instruct has an ECI of 139.9.
Which is better for coding?
There are no published SWE-bench Verified results for Baichuan M2 32B, DeepSeek R1 0528 Qwen3 8B and Kimi K2 Instruct yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Note that Baichuan M2 32B and DeepSeek R1 0528 Qwen3 8B does not support tool calling, which most coding agents need.
Which has the bigger context window?
Baichuan M2 32B, DeepSeek R1 0528 Qwen3 8B and Kimi K2 Instruct share the same 131,072-token context window. Maximum output per response: Baichuan M2 32B up to 131,072, DeepSeek R1 0528 Qwen3 8B up to 32,000, Kimi K2 Instruct up to 32,768 tokens.
Which can read images, PDFs, audio or video?
Baichuan M2 32B accepts text; DeepSeek R1 0528 Qwen3 8B accepts text; Kimi K2 Instruct accepts text. They handle the same number of input types.
Are any of these open source?
Yes, all three publish their weights (Apache-2.0, MIT and Modified MIT), so you can self-host them.
Which is newer?
Baichuan M2 32B is the newest, released Aug 13, 2025. Kimi K2 Instruct came out Jul 11, 2025; DeepSeek R1 0528 Qwen3 8B came out May 29, 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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