QwQ 32B vs Kimi K2 Instruct
Too close to call on our weighted score (QwQ 32B 53, Kimi K2 Instruct 50). The right pick depends on what you value most.
Alibaba (Qwen)QwQ 32B
53/100- ECI137.5
- Price$0.53 / $0.70
- Context131K
Moonshot AIKimi K2 Instruct
50/100- ECI139.9
- Price$0.57 / $2.30
- Context131K
Add a model
Make it a three-way comparison.
Too close to call
It is close. Our weighted score puts them within 3 points (QwQ 32B 53/100, Kimi K2 Instruct 50/100), so choose by what matters most for your work: Kimi K2 Instruct for raw capability and QwQ 32B on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityKimi K2 InstructCapabilities Index (ECI): Kimi K2 Instruct 139.9 · QwQ 32B 137.5
- Lowest priceQwQ 32BQwQ 32B $0.573 · Kimi K2 Instruct $1.00 per 1M tokens (3:1 blend)
- Longest contextAbout the sameQwQ 32B 131,072 · Kimi K2 Instruct 131,072 tokens
- Widest inputsSame inputsQwQ 32B: Text · Kimi K2 Instruct: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | QwQ 32B | Kimi K2 Instruct |
|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 61 | 64 |
| Price | 25% | 59 | 48 |
| Inputs & features | 15% | 35 | 25 |
| Context window | 10% | 25 | 25 |
| Overall | 100% | 53/100 | 50/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | QwQ 32BAlibaba (Qwen) | Kimi K2 InstructMoonshot AI |
|---|---|---|
| Capability | ||
| Capabilities Index (ECI) | 137.5 | 139.9 (best) |
| ECI rank | #123 of 165 | #112 of 165 (best) |
| GPQA DiamondGraduate-level science questions | 65.3% | — |
| OTIS Mock AIME 2024–2025Competition mathematics | 59.2% | — |
| Price per million tokens | ||
| Input | $0.53 (best) | $0.57 |
| Output | $0.70 (best) | $2.30 |
| Cached input | — | — |
| Blended (3:1) | $0.573 (best) | $1.00 |
| Long-context rate | Same rate | Same rate |
| Price source | Median of 2 providers | Median of 11 providers |
| Limits | ||
| Context window | 131,072 tokens | 131,072 tokens |
| Max output | 8,192 tokens | 32,768 tokens (best) |
| Inputs and features | ||
| Text | Yes | Yes |
| Images | No | No |
| PDFs | No | No |
| Audio | No | No |
| Video | No | No |
| Reasoning | Yes | No |
| Tool calling | Yes | Yes |
| Structured output | No | No |
| Availability | ||
| Weights | Open | OpenModified MIT |
| API model ID | — | — |
| API providers | 2 | 11 (best) |
| Released | Mar 5, 2025 | Jul 11, 2025 |
| Knowledge cutoff | Apr 2024 | — |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- QwQ 32B$6.70
- Kimi K2 Instruct$10.30
Which should you choose?
Which is better: QwQ 32B or Kimi K2 Instruct?
It is close. Our weighted score puts them within 3 points (QwQ 32B 53/100, Kimi K2 Instruct 50/100), so choose by what matters most for your work: Kimi K2 Instruct for raw capability and QwQ 32B on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, QwQ 32B or Kimi K2 Instruct?
QwQ 32B is cheaper at $0.53 input / $0.70 output per million tokens (median across 2 API providers). 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 $0.573 per million tokens for QwQ 32B versus $1.00 for Kimi K2 Instruct (1.8× as much).
Which scores higher on benchmarks?
Kimi K2 Instruct scores higher on the Capabilities Index (ECI): Kimi K2 Instruct 139.9 (#112 of 165) and QwQ 32B 137.5 (#123 of 165). The confidence ranges of the top two overlap (136.3–141.6 vs 133.1–141.3), so treat the gap as small.
Which is better for coding?
There are no published SWE-bench Verified results for QwQ 32B and Kimi K2 Instruct yet, so there is no like-for-like coding score. On overall capability, Kimi K2 Instruct 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?
QwQ 32B and Kimi K2 Instruct share the same 131,072-token context window. Maximum output per response: QwQ 32B up to 8,192, Kimi K2 Instruct up to 32,768 tokens.
Which can read images, PDFs, audio or video?
QwQ 32B accepts text; Kimi K2 Instruct accepts text. They handle the same number of input types.
Are any of these open source?
Yes, both publish their weights (Modified MIT), so you can self-host them.
Which is newer?
Kimi K2 Instruct is the newest, released Jul 11, 2025. QwQ 32B came out Mar 5, 2025. Knowledge cutoff: QwQ 32B Apr 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 Nov 2, 2026.
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