GLM-4.7 vs Qwen3 14B vs Kimi K2 Thinking
Too close to call on our weighted score (Kimi K2 Thinking 58, GLM-4.7 56, Qwen3 14B 54). The right pick depends on what you value most.
Z.ai (Zhipu)GLM-4.7
56/100- ECI143.5
- Price$0.60 / $2.20
- Context205K
Alibaba (Qwen)Qwen3 14B
54/100- ECI138.2
- Price$0.35 / $1.40
- Context131K
Moonshot AIKimi K2 Thinking
58/100- ECI146.0
- Price$0.60 / $2.50
- Context262K
Too close to call
It is close. Our weighted score puts them within 2 points (Kimi K2 Thinking 58/100, GLM-4.7 56/100, Qwen3 14B 54/100), so choose by what matters most for your work: Kimi K2 Thinking for raw capability and Qwen3 14B on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityKimi K2 ThinkingCapabilities Index (ECI): Kimi K2 Thinking 146.0 · GLM-4.7 143.5 · Qwen3 14B 138.2
- Lowest priceQwen3 14BQwen3 14B $0.613 · GLM-4.7 $1.00 · Kimi K2 Thinking $1.07 per 1M tokens (3:1 blend)
- Longest contextKimi K2 ThinkingKimi K2 Thinking 262,144 · GLM-4.7 204,800 · Qwen3 14B 131,072 tokens
- Widest inputsSame inputsGLM-4.7: Text · Qwen3 14B: 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 14B | Kimi K2 Thinking |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 70 | 63 | 73 |
| Price | 25% | 50 | 60 | 48 |
| Inputs & features | 15% | 35 | 35 | 35 |
| Context window | 10% | 32 | 24 | 37 |
| Overall | 100% | 56/100 | 54/100 | 58/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | GLM-4.7Z.ai (Zhipu) | Qwen3 14BAlibaba (Qwen) | Kimi K2 ThinkingMoonshot AI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 143.5 | 138.2 | 146.0 (best) |
| ECI rank | #84 of 148 | #107 of 148 | #72 of 148 (best) |
| GPQA DiamondGraduate-level science questions | 83.3% | 63.8% | 84.2% (best) |
| OTIS Mock AIME 2024–2025Competition mathematics | 83.3% (best) | 66.4% | 83.1% |
| SimpleQA VerifiedShort factual questions | 32.2% | — | — |
| Price per million tokens | |||
| Input | $0.60 | $0.35 (best) | $0.60 |
| Output | $2.20 | $1.40 (best) | $2.50 |
| Cached input | $0.11 | — | — |
| Blended (3:1) | $1.00 | $0.613 (best) | $1.07 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Z.AI API | Official Alibaba API | Median of 10 providers |
| Limits | |||
| Context window | 204,800 tokens | 131,072 tokens | 262,144 tokens (best) |
| Max output | 131,072 tokens | 8,192 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 | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Open | Open | Open |
| API model ID | glm-4.7 | qwen3-14b | — |
| API providers | 20 (best) | 1 | 10 |
| Released | Dec 22, 2025 | Apr 29, 2025 | Nov 6, 2025 |
| Knowledge cutoff | Apr 2025 | Apr 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 14B$6.30
- Kimi K2 Thinking$11.00
Which should you choose?
Which is better: GLM-4.7, Qwen3 14B or Kimi K2 Thinking?
It is close. Our weighted score puts them within 2 points (Kimi K2 Thinking 58/100, GLM-4.7 56/100, Qwen3 14B 54/100), so choose by what matters most for your work: Kimi K2 Thinking for raw capability and Qwen3 14B on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, GLM-4.7, Qwen3 14B or Kimi K2 Thinking?
Qwen3 14B is cheaper at $0.35 input / $1.40 output per million tokens (official Alibaba API price). 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 $0.613 per million tokens for Qwen3 14B versus $1.00 for GLM-4.7 (1.6× as much) and $1.07 for Kimi K2 Thinking (1.8× as much).
Which scores higher on benchmarks?
Kimi K2 Thinking scores higher on the Capabilities Index (ECI): Kimi K2 Thinking 146.0 (#72 of 148), GLM-4.7 143.5 (#84 of 148) and Qwen3 14B 138.2 (#107 of 148). The confidence ranges of the top two overlap (143.4–147.6 vs 141.3–145.6), so treat the gap as small. On individual benchmarks: GPQA Diamond — Kimi K2 Thinking 84.2%, GLM-4.7 83.3%, Qwen3 14B 63.8%; OTIS Mock AIME 2024–2025 — GLM-4.7 83.3%, Kimi K2 Thinking 83.1%, Qwen3 14B 66.4%.
Which is better for coding?
There are no published SWE-bench Verified results for GLM-4.7, Qwen3 14B and Kimi K2 Thinking yet, so there is no like-for-like coding score. On overall capability, Kimi K2 Thinking 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?
Kimi K2 Thinking has the largest context window at 262,144 tokens, against 204,800 for GLM-4.7 and 131,072 for Qwen3 14B. Maximum output per response: GLM-4.7 up to 131,072, Qwen3 14B up to 8,192, Kimi K2 Thinking up to 262,144 tokens.
Which can read images, PDFs, audio or video?
GLM-4.7 accepts text; Qwen3 14B 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?
GLM-4.7 is the newest, released Dec 22, 2025. Kimi K2 Thinking came out Nov 6, 2025; Qwen3 14B came out Apr 29, 2025. Knowledge cutoff: GLM-4.7 Apr 2025, Qwen3 14B Apr 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.
