GLM-4.7 vs Qwen3 Coder Next vs Kimi K2 Thinking
Qwen3 Coder Next comes out ahead, 51 to 42 and 42 on our weighted score, and it is the cheaper option too.
Z.ai (Zhipu)GLM-4.7
42/100- ECI143.5
- Price$0.60 / $2.20
- Context205K
- Our pick
Alibaba (Qwen)Qwen3 Coder Next
51/100- ECI—
- Price$0.20 / $1.20
- Context262K
Moonshot AIKimi K2 Thinking
42/100- ECI146.0
- Price$0.60 / $2.50
- Context262K
Qwen3 Coder Next is our pick
Qwen3 Coder Next is the better all-round choice, scoring 51/100 against Kimi K2 Thinking (42) and GLM-4.7 (42). It leads on price. The score weighs price 50%, inputs & features 30%, context window 20%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
- CapabilityNot enough dataNo independent benchmark covers every model here yet
- Lowest priceQwen3 Coder NextQwen3 Coder Next $0.45 · GLM-4.7 $1.00 · Kimi K2 Thinking $1.07 per 1M tokens (3:1 blend)
- Longest contextQwen3 Coder Next and Kimi K2 ThinkingQwen3 Coder Next 262,144 · Kimi K2 Thinking 262,144 · GLM-4.7 204,800 tokens
- Widest inputsSame inputsGLM-4.7: Text · Qwen3 Coder Next: Text · Kimi K2 Thinking: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | GLM-4.7 | Qwen3 Coder Next | Kimi K2 Thinking |
|---|---|---|---|---|
| Price | 50% | 50 | 66 | 48 |
| Inputs & features | 30% | 35 | 35 | 35 |
| Context window | 20% | 32 | 37 | 37 |
| Overall | 100% | 42/100 | 51/100 | 42/100 |
Left out because at least one model lacks the data: capability. The remaining weights were rescaled.
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | GLM-4.7Z.ai (Zhipu) | Qwen3 Coder NextAlibaba (Qwen) | Kimi K2 ThinkingMoonshot AI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 143.5 | — | 146.0 (best) |
| ECI rank | #84 of 148 | — | #72 of 148 (best) |
| GPQA DiamondGraduate-level science questions | 83.3% | — | 84.2% (best) |
| OTIS Mock AIME 2024–2025Competition mathematics | 83.3% (best) | — | 83.1% |
| SimpleQA VerifiedShort factual questions | 32.2% | — | — |
| Price per million tokens | |||
| Input | $0.60 | $0.20 (best) | $0.60 |
| Output | $2.20 | $1.20 (best) | $2.50 |
| Cached input | $0.11 | — | — |
| Blended (3:1) | $1.00 | $0.45 (best) | $1.07 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Z.AI API | Median of 11 providers | Median of 10 providers |
| Limits | |||
| Context window | 204,800 tokens | 262,144 tokens (best) | 262,144 tokens (best) |
| Max output | 131,072 tokens | 65,536 tokens | 262,144 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 | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | Yes | No |
| Availability | |||
| Weights | Open | Open | Open |
| API model ID | glm-4.7 | — | — |
| API providers | 20 (best) | 11 | 10 |
| Released | Dec 22, 2025 | Feb 3, 2026 | Nov 6, 2025 |
| Knowledge cutoff | Apr 2025 | Sep 2025 | Aug 2024 |
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
- Qwen3 Coder Next$4.40
- Kimi K2 Thinking$11.00
Which should you choose?
Which is better: GLM-4.7, Qwen3 Coder Next or Kimi K2 Thinking?
Qwen3 Coder Next is the better all-round choice, scoring 51/100 against Kimi K2 Thinking (42) and GLM-4.7 (42). It leads on price. The score weighs price 50%, inputs & features 30%, context window 20%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
Which is cheaper, GLM-4.7, Qwen3 Coder Next or Kimi K2 Thinking?
Qwen3 Coder Next is cheaper at $0.20 input / $1.20 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 Thinking costs $0.60 input / $2.50 output per million tokens (median across 10 API providers). At a typical mix of three input tokens to one output token, that is $0.45 per million tokens for Qwen3 Coder Next versus $1.00 for GLM-4.7 (2.2× as much) and $1.07 for Kimi K2 Thinking (2.4× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. GLM-4.7 has an ECI of 143.5, Qwen3 Coder Next has not been scored yet and Kimi K2 Thinking has an ECI of 146.0.
Which is better for coding?
There are no published SWE-bench Verified results for GLM-4.7, Qwen3 Coder Next and Kimi K2 Thinking yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. All three support tool calling for agent workflows.
Which has the bigger context window?
Qwen3 Coder Next and Kimi K2 Thinking have the largest context windows (262,144 and 262,144 tokens), against 204,800 for GLM-4.7. Maximum output per response: GLM-4.7 up to 131,072, Qwen3 Coder Next up to 65,536, Kimi K2 Thinking up to 262,144 tokens.
Which can read images, PDFs, audio or video?
GLM-4.7 accepts text; Qwen3 Coder Next accepts text; Kimi K2 Thinking accepts text. They handle the same number of input types.
Are any of these open source?
Yes, all three publish their weights, so you can self-host them.
Which is newer?
Qwen3 Coder Next is the newest, released Feb 3, 2026. GLM-4.7 came out Dec 22, 2025; Kimi K2 Thinking came out Nov 6, 2025. Knowledge cutoff: GLM-4.7 Apr 2025, Qwen3 Coder Next Sep 2025, Kimi K2 Thinking Aug 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 Oct 4, 2026.
