GLM-4.7 vs Kimi K2 Instruct vs Qwen3 235B-A22B Thinking 2507
Too close to call on our weighted score (Qwen3 235B-A22B Thinking 2507 55, GLM-4.7 55, Kimi K2 Instruct 50). The right pick depends on what you value most.
Z.ai (Zhipu)GLM-4.7
55/100- ECI143.7
- Price$0.60 / $2.20
- Context205K
Moonshot AIKimi K2 Instruct
50/100- ECI139.9
- Price$0.57 / $2.30
- Context131K
Alibaba (Qwen)Qwen3 235B-A22B Thinking 2507
55/100- ECI143.8
- Price$0.30 / $2.90
- Context262K
Too close to call
It is close. Our weighted score puts them within a point (Qwen3 235B-A22B Thinking 2507 55/100, GLM-4.7 55/100, Kimi K2 Instruct 50/100), so choose by what matters most for your work: Qwen3 235B-A22B Thinking 2507 for raw capability. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityQwen3 235B-A22B Thinking 2507Capabilities Index (ECI): Qwen3 235B-A22B Thinking 2507 143.8 · GLM-4.7 143.7 · Kimi K2 Instruct 139.9
- Lowest priceQwen3 235B-A22B Thinking 2507Qwen3 235B-A22B Thinking 2507 $0.95 · GLM-4.7 $1.00 · Kimi K2 Instruct $1.00 per 1M tokens (3:1 blend)
- Longest contextQwen3 235B-A22B Thinking 2507Qwen3 235B-A22B Thinking 2507 262,144 · GLM-4.7 204,800 · Kimi K2 Instruct 131,072 tokens
- Widest inputsSame inputsGLM-4.7: Text · Kimi K2 Instruct: Text · Qwen3 235B-A22B Thinking 2507: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | GLM-4.7 | Kimi K2 Instruct | Qwen3 235B-A22B Thinking 2507 |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 69 | 64 | 69 |
| Price | 25% | 48 | 48 | 49 |
| Inputs & features | 15% | 35 | 25 | 35 |
| Context window | 10% | 32 | 25 | 37 |
| Overall | 100% | 55/100 | 50/100 | 55/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | GLM-4.7Z.ai (Zhipu) | Kimi K2 InstructMoonshot AI | Qwen3 235B-A22B Thinking 2507Alibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 143.7 | 139.9 | 143.8 (best) |
| ECI rank | #94 of 165 | #112 of 165 | #93 of 165 (best) |
| GPQA DiamondGraduate-level science questions | 83.3% (best) | — | 80.1% |
| OTIS Mock AIME 2024–2025Competition mathematics | 83.3% | — | 86.7% (best) |
| SimpleQA VerifiedShort factual questions | 32.2% | — | 40.4% (best) |
| Price per million tokens | |||
| Input | $0.60 | $0.57 | $0.30 (best) |
| Output | $2.20 (best) | $2.30 | $2.90 |
| Cached input | $0.11 | — | — |
| Blended (3:1) | $1.00 | $1.00 | $0.95 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Z.AI API | Median of 11 providers | Median of 11 providers |
| Limits | |||
| Context window | 204,800 tokens | 131,072 tokens | 262,144 tokens (best) |
| Max output | 131,072 tokens (best) | 32,768 tokens | 81,920 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 | No | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Open | OpenModified MIT | OpenApache 2.0 |
| API model ID | glm-4.7 | — | — |
| API providers | 31 (best) | 11 | 12 |
| Released | Dec 22, 2025 | Jul 11, 2025 | Jul 25, 2025 |
| Knowledge cutoff | Apr 2025 | — | — |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- GLM-4.7$10.40
- Kimi K2 Instruct$10.30
- Qwen3 235B-A22B Thinking 2507$8.80
Which should you choose?
Which is better: GLM-4.7, Kimi K2 Instruct or Qwen3 235B-A22B Thinking 2507?
It is close. Our weighted score puts them within a point (Qwen3 235B-A22B Thinking 2507 55/100, GLM-4.7 55/100, Kimi K2 Instruct 50/100), so choose by what matters most for your work: Qwen3 235B-A22B Thinking 2507 for raw capability. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, GLM-4.7, Kimi K2 Instruct or Qwen3 235B-A22B Thinking 2507?
Qwen3 235B-A22B Thinking 2507 is cheaper at $0.30 input / $2.90 output per million tokens (median across 11 API providers). 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 $0.95 per million tokens for Qwen3 235B-A22B Thinking 2507 versus $1.00 for GLM-4.7 (1.1× as much) and $1.00 for Kimi K2 Instruct (1.1× as much).
Which scores higher on benchmarks?
Qwen3 235B-A22B Thinking 2507 scores higher on the Capabilities Index (ECI): Qwen3 235B-A22B Thinking 2507 143.8 (#93 of 165), GLM-4.7 143.7 (#94 of 165) and Kimi K2 Instruct 139.9 (#112 of 165). The confidence ranges of the top two overlap (141.9–145.2 vs 141.4–145.8), so treat the gap as small.
Which is better for coding?
There are no published SWE-bench Verified results for GLM-4.7, Kimi K2 Instruct and Qwen3 235B-A22B Thinking 2507 yet, so there is no like-for-like coding score. On overall capability, Qwen3 235B-A22B Thinking 2507 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?
Qwen3 235B-A22B Thinking 2507 has the largest context window at 262,144 tokens, against 204,800 for GLM-4.7 and 131,072 for Kimi K2 Instruct. Maximum output per response: GLM-4.7 up to 131,072, Kimi K2 Instruct up to 32,768, Qwen3 235B-A22B Thinking 2507 up to 81,920 tokens.
Which can read images, PDFs, audio or video?
GLM-4.7 accepts text; Kimi K2 Instruct accepts text; Qwen3 235B-A22B Thinking 2507 accepts text. They handle the same number of input types.
Are any of these open source?
Yes, all three publish their weights (Modified MIT and Apache 2.0), so you can self-host them.
Which is newer?
GLM-4.7 is the newest, released Dec 22, 2025. Qwen3 235B-A22B Thinking 2507 came out Jul 25, 2025; Kimi K2 Instruct came out Jul 11, 2025. Knowledge cutoff: GLM-4.7 Apr 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.
Related comparisons
- GLM-4.7 vs Kimi K2 ThinkingMoonshot AI · ECI 145.9
- GLM-4.7 vs Nemotron 3 Ultra 550B A55BNVIDIA · ECI 146.0
- GLM-4.7 vs Gemini 3.5 Flash LiteGoogle · ECI 145.0
- Kimi K2 Instruct vs Gemini 2.5 FlashGoogle · ECI 140.6
- Kimi K2 Instruct vs DeepSeek R1 0528DeepSeek · ECI 141.2
- Qwen3 235B-A22B Thinking 2507 vs GPT-5 MiniOpenAI · ECI 145.4
