QwQ 32B vs Kimi K2 Instruct vs DeepSeek V3 0324
Too close to call on our weighted score (QwQ 32B 53, DeepSeek V3 0324 52, 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
DeepSeekDeepSeek V3 0324
52/100- ECI135.9
- Price$0.275 / $1.12
- Context164K
Too close to call
It is close. Our weighted score puts them within 1 points (QwQ 32B 53/100, DeepSeek V3 0324 52/100, Kimi K2 Instruct 50/100), so choose by what matters most for your work: Kimi K2 Instruct for raw capability, DeepSeek V3 0324 on price and DeepSeek V3 0324 for long inputs. 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 · DeepSeek V3 0324 135.9
- Lowest priceDeepSeek V3 0324DeepSeek V3 0324 $0.486 · QwQ 32B $0.573 · Kimi K2 Instruct $1.00 per 1M tokens (3:1 blend)
- Longest contextDeepSeek V3 0324DeepSeek V3 0324 163,840 · QwQ 32B 131,072 · Kimi K2 Instruct 131,072 tokens
- Widest inputsSame inputsQwQ 32B: Text · Kimi K2 Instruct: Text · DeepSeek V3 0324: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | QwQ 32B | Kimi K2 Instruct | DeepSeek V3 0324 |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 61 | 64 | 59 |
| Price | 25% | 59 | 48 | 62 |
| Inputs & features | 15% | 35 | 25 | 25 |
| Context window | 10% | 25 | 25 | 28 |
| Overall | 100% | 53/100 | 50/100 | 52/100 |
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 | DeepSeek V3 0324DeepSeek |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 137.5 | 139.9 (best) | 135.9 |
| ECI rank | #123 of 165 | #112 of 165 (best) | #128 of 165 |
| GPQA DiamondGraduate-level science questions | 65.3% | — | 67.6% (best) |
| OTIS Mock AIME 2024–2025Competition mathematics | 59.2% (best) | — | 37.8% |
| Price per million tokens | |||
| Input | $0.53 | $0.57 | $0.275 (best) |
| Output | $0.70 (best) | $2.30 | $1.12 |
| Cached input | — | — | — |
| Blended (3:1) | $0.573 | $1.00 | $0.486 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 2 providers | Median of 11 providers | Median of 13 providers |
| Limits | |||
| Context window | 131,072 tokens | 131,072 tokens | 163,840 tokens (best) |
| Max output | 8,192 tokens | 32,768 tokens | 163,840 tokens (best) |
| 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 | No | No |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Open | OpenModified MIT | Open |
| API model ID | — | — | — |
| API providers | 2 | 11 | 13 (best) |
| Released | Mar 5, 2025 | Jul 11, 2025 | Mar 24, 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
- DeepSeek V3 0324$4.99
Which should you choose?
Which is better: QwQ 32B, Kimi K2 Instruct or DeepSeek V3 0324?
It is close. Our weighted score puts them within 1 points (QwQ 32B 53/100, DeepSeek V3 0324 52/100, Kimi K2 Instruct 50/100), so choose by what matters most for your work: Kimi K2 Instruct for raw capability, DeepSeek V3 0324 on price and DeepSeek V3 0324 for long inputs. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, QwQ 32B, Kimi K2 Instruct or DeepSeek V3 0324?
DeepSeek V3 0324 is cheaper at $0.275 input / $1.12 output per million tokens (median across 13 API providers). QwQ 32B costs $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 $0.486 per million tokens for DeepSeek V3 0324 versus $0.573 for QwQ 32B (1.2× as much) and $1.00 for Kimi K2 Instruct (2.1× 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), QwQ 32B 137.5 (#123 of 165) and DeepSeek V3 0324 135.9 (#128 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, Kimi K2 Instruct and DeepSeek V3 0324 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. All three support tool calling for agent workflows.
Which has the bigger context window?
DeepSeek V3 0324 has the largest context window at 163,840 tokens, against 131,072 for QwQ 32B and 131,072 for Kimi K2 Instruct. Maximum output per response: QwQ 32B up to 8,192, Kimi K2 Instruct up to 32,768, DeepSeek V3 0324 up to 163,840 tokens.
Which can read images, PDFs, audio or video?
QwQ 32B accepts text; Kimi K2 Instruct accepts text; DeepSeek V3 0324 accepts text. They handle the same number of input types.
Are any of these open source?
Yes, all three publish their weights (Modified MIT), so you can self-host them.
Which is newer?
Kimi K2 Instruct is the newest, released Jul 11, 2025. DeepSeek V3 0324 came out Mar 24, 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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