Kimi K2 Instruct vs GLM-4.7 vs Gemini 2.5 Flash
Gemini 2.5 Flash comes out ahead, 66 to 55 and 50 on our weighted score, and it is the cheaper option too.
Moonshot AIKimi K2 Instruct
50/100- ECI139.9
- Price$0.57 / $2.30
- Context131K
Z.ai (Zhipu)GLM-4.7
55/100- ECI143.7
- Price$0.60 / $2.20
- Context205K
- 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 GLM-4.7 (55) and Kimi K2 Instruct (50). It leads on price, inputs & features and context window. GLM-4.7 wins on capability. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityGLM-4.7Capabilities Index (ECI): GLM-4.7 143.7 · Gemini 2.5 Flash 140.6 · Kimi K2 Instruct 139.9
- Lowest priceGemini 2.5 FlashGemini 2.5 Flash $0.85 · GLM-4.7 $1.00 · Kimi K2 Instruct $1.00 per 1M tokens (3:1 blend)
- Longest contextGemini 2.5 FlashGemini 2.5 Flash 1,048,576 · GLM-4.7 204,800 · Kimi K2 Instruct 131,072 tokens
- Widest inputsGemini 2.5 FlashKimi K2 Instruct: Text · GLM-4.7: Text · Gemini 2.5 Flash: Text, Images, PDFs, Audio, Video
- Self-hostingKimi K2 Instruct and GLM-4.7Publishes downloadable weights (Modified MIT)
| Measure | Weight | Kimi K2 Instruct | GLM-4.7 | Gemini 2.5 Flash |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 64 | 69 | 65 |
| Price | 25% | 48 | 48 | 51 |
| Inputs & features | 15% | 25 | 35 | 100 |
| Context window | 10% | 25 | 32 | 61 |
| Overall | 100% | 50/100 | 55/100 | 66/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Kimi K2 InstructMoonshot AI | GLM-4.7Z.ai (Zhipu) | Gemini 2.5 FlashGoogle |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 139.9 | 143.7 (best) | 140.6 |
| ECI rank | #112 of 165 | #94 of 165 (best) | #110 of 165 |
| GPQA DiamondGraduate-level science questions | — | 83.3% | — |
| OTIS Mock AIME 2024–2025Competition mathematics | — | 83.3% | — |
| SimpleQA VerifiedShort factual questions | — | 32.2% | — |
| Price per million tokens | |||
| Input | $0.57 | $0.60 | $0.30 (best) |
| Output | $2.30 | $2.20 (best) | $2.50 |
| Cached input | — | $0.11 | $0.03 (best) |
| Blended (3:1) | $1.00 | $1.00 | $0.85 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 11 providers | Official Z.AI API | Official Google API |
| Limits | |||
| Context window | 131,072 tokens | 204,800 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 | Yes | Yes |
| Structured output | No | No | Yes |
| Availability | |||
| Weights | OpenModified MIT | Open | Proprietary |
| API model ID | — | glm-4.7 | gemini-2.5-flash |
| API providers | 11 | 31 | 32 (best) |
| Released | Jul 11, 2025 | Dec 22, 2025 | Jun 17, 2025 |
| Knowledge cutoff | — | Apr 2025 | 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
- GLM-4.7$10.40
- Gemini 2.5 Flash$8.00
Which should you choose?
Which is better: Kimi K2 Instruct, GLM-4.7 or Gemini 2.5 Flash?
Gemini 2.5 Flash is the better all-round choice, scoring 66/100 against GLM-4.7 (55) and Kimi K2 Instruct (50). It leads on price, inputs & features and context window. GLM-4.7 wins on capability. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, Kimi K2 Instruct, GLM-4.7 or Gemini 2.5 Flash?
Gemini 2.5 Flash is cheaper at $0.30 input / $2.50 output per million tokens (official Google API price). GLM-4.7 costs $0.60 input / $2.20 output per million tokens (official Z.AI 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 $0.85 per million tokens for Gemini 2.5 Flash versus $1.00 for GLM-4.7 (1.2× as much) and $1.00 for Kimi K2 Instruct (1.2× as much).
Which scores higher on benchmarks?
GLM-4.7 scores higher on the Capabilities Index (ECI): GLM-4.7 143.7 (#94 of 165), Gemini 2.5 Flash 140.6 (#110 of 165) and Kimi K2 Instruct 139.9 (#112 of 165). The confidence ranges of the top two overlap (141.4–145.8 vs 138.4–142.1), so treat the gap as small.
Which is better for coding?
There are no published SWE-bench Verified results for Kimi K2 Instruct, GLM-4.7 and Gemini 2.5 Flash yet, so there is no like-for-like coding score. On overall capability, GLM-4.7 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 204,800 for GLM-4.7 and 131,072 for Kimi K2 Instruct. Maximum output per response: Kimi K2 Instruct up to 32,768, GLM-4.7 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; GLM-4.7 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 GLM-4.7 publishes its weights (Modified MIT) and can be self-hosted; Gemini 2.5 Flash is proprietary.
Which is newer?
GLM-4.7 is the newest, released Dec 22, 2025. Kimi K2 Instruct came out Jul 11, 2025; Gemini 2.5 Flash came out Jun 17, 2025. Knowledge cutoff: GLM-4.7 Apr 2025, 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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