Kimi K2 Instruct vs QwQ 32B vs Gemini 2.5 Flash
Gemini 2.5 Flash comes out ahead, 66 to 53 and 50 on our weighted score, though QwQ 32B is 33% cheaper per token.
Moonshot AIKimi K2 Instruct
50/100- ECI139.9
- Price$0.57 / $2.30
- Context131K
Alibaba (Qwen)QwQ 32B
53/100- ECI137.5
- Price$0.53 / $0.70
- Context131K
- Our pick
GoogleGemini 2.5 Flash
66/100- 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 66/100 against QwQ 32B (53) and Kimi K2 Instruct (50). It leads on inputs & features and context window. QwQ 32B wins on price. 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 · QwQ 32B 137.5
- Lowest priceQwQ 32BQwQ 32B $0.573 · 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 · QwQ 32B 131,072 tokens
- Widest inputsGemini 2.5 FlashKimi K2 Instruct: Text · QwQ 32B: Text · Gemini 2.5 Flash: Text, Images, PDFs, Audio, Video
- Self-hostingKimi K2 Instruct and QwQ 32BPublishes downloadable weights (Modified MIT)
| Measure | Weight | Kimi K2 Instruct | QwQ 32B | Gemini 2.5 Flash |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 64 | 61 | 65 |
| Price | 25% | 48 | 59 | 51 |
| Inputs & features | 15% | 25 | 35 | 100 |
| Context window | 10% | 25 | 25 | 61 |
| Overall | 100% | 50/100 | 53/100 | 66/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Kimi K2 InstructMoonshot AI | QwQ 32BAlibaba (Qwen) | Gemini 2.5 FlashGoogle |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 139.9 | 137.5 | 140.6 (best) |
| ECI rank | #112 of 165 | #123 of 165 | #110 of 165 (best) |
| GPQA DiamondGraduate-level science questions | — | 65.3% | — |
| OTIS Mock AIME 2024–2025Competition mathematics | — | 59.2% | — |
| Price per million tokens | |||
| Input | $0.57 | $0.53 | $0.30 (best) |
| Output | $2.30 | $0.70 (best) | $2.50 |
| Cached input | — | — | $0.03 |
| Blended (3:1) | $1.00 | $0.573 (best) | $0.85 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 11 providers | Median of 2 providers | Official Google API |
| Limits | |||
| Context window | 131,072 tokens | 131,072 tokens | 1,048,576 tokens (best) |
| Max output | 32,768 tokens | 8,192 tokens | 65,536 tokens (best) |
| 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 | Yes | Yes |
| Structured output | No | No | Yes |
| Availability | |||
| Weights | OpenModified MIT | Open | Proprietary |
| API model ID | — | — | gemini-2.5-flash |
| API providers | 11 | 2 | 32 (best) |
| Released | Jul 11, 2025 | Mar 5, 2025 | Jun 17, 2025 |
| Knowledge cutoff | — | Apr 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.
- Kimi K2 Instruct$10.30
- QwQ 32B$6.70
- Gemini 2.5 Flash$8.00
Which should you choose?
Which is better: Kimi K2 Instruct, QwQ 32B or Gemini 2.5 Flash?
Gemini 2.5 Flash is the better all-round choice, scoring 66/100 against QwQ 32B (53) and Kimi K2 Instruct (50). It leads on inputs & features and context window. QwQ 32B wins on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, Kimi K2 Instruct, QwQ 32B or Gemini 2.5 Flash?
QwQ 32B is cheaper at $0.53 input / $0.70 output per million tokens (median across 2 API providers). 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 $0.573 per million tokens for QwQ 32B versus $0.85 for Gemini 2.5 Flash (1.5× as much) and $1.00 for Kimi K2 Instruct (1.8× 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 QwQ 32B 137.5 (#123 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 Kimi K2 Instruct, QwQ 32B and Gemini 2.5 Flash 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 131,072 for QwQ 32B. Maximum output per response: Kimi K2 Instruct up to 32,768, QwQ 32B up to 8,192, Gemini 2.5 Flash up to 65,536 tokens.
Which can read images, PDFs, audio or video?
Kimi K2 Instruct accepts text; QwQ 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 QwQ 32B 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; QwQ 32B came out Mar 5, 2025. Knowledge cutoff: QwQ 32B Apr 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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