Kimi K3 vs GLM-5.3
Too close to call on our weighted score (Kimi K3 65, GLM-5.3 63). The right pick depends on what you value most.
Too close to call
It is close. Our weighted score puts them within 2 points (Kimi K3 65/100, GLM-5.3 63/100), so choose by what matters most for your work: Kimi K3 for raw capability and GLM-5.3 on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityKimi K3Capabilities Index (ECI): Kimi K3 158.3 · GLM-5.3 156.2
- Lowest priceGLM-5.3GLM-5.3 $2.15 · Kimi K3 $6.00 per 1M tokens (3:1 blend)
- Longest contextKimi K3Kimi K3 1,048,576 · GLM-5.3 1,000,000 tokens
- Widest inputsKimi K3Kimi K3: Text, Images, Video · GLM-5.3: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Kimi K3 | GLM-5.3 |
|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 87 | 84 |
| Price | 25% | 12 | 33 |
| Inputs & features | 15% | 80 | 45 |
| Context window | 10% | 61 | 60 |
| Overall | 100% | 65/100 | 63/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification |  Kimi K3Moonshot AI |  GLM-5.3Z.ai (Zhipu) |
|---|---|---|
| Capability | ||
| Capabilities Index (ECI) | 158.3 (best) | 156.2 |
| ECI rank | #20 of 165 (best) | #33 of 165 |
| GPQA DiamondGraduate-level science questions | 93.1% (best) | 90.9% |
| FrontierMath Tiers 1–3Research-level mathematics | 72.2% (best) | 68.8% |
| OTIS Mock AIME 2024–2025Competition mathematics | 97.2% (best) | 91.1% |
| SimpleQA VerifiedShort factual questions | 50.6% (best) | 41.0% |
| Price per million tokens | ||
| Input | $3.00 | $1.40 (best) |
| Output | $15.00 | $4.40 (best) |
| Cached input | $0.30 | $0.26 (best) |
| Blended (3:1) | $6.00 | $2.15 (best) |
| Long-context rate | Same rate | Same rate |
| Price source | Official Moonshot AI API | Official Z.AI API |
| Limits | ||
| Context window | 1,048,576 tokens (best) | 1,000,000 tokens |
| Max output | 131,072 tokens | 131,072 tokens |
| Inputs and features | ||
| Text | Yes | Yes |
| Images | Yes | No |
| PDFs | No | No |
| Audio | No | No |
| Video | Yes | No |
| Reasoning | Yeslow · high · max | Yeslow · high · max |
| Tool calling | Yes | Yes |
| Structured output | Yes | Yes |
| Availability | ||
| Weights | Open | Open |
| API model ID | kimi-k3 | glm-5.3 |
| API providers | 76 | 83 (best) |
| Released | Jul 16, 2026 | Aug 14, 2026 |
| Knowledge cutoff | — | — |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Kimi K3$60.00
- GLM-5.3$22.80
Which should you choose?
Which is better: Kimi K3 or GLM-5.3?
It is close. Our weighted score puts them within 2 points (Kimi K3 65/100, GLM-5.3 63/100), so choose by what matters most for your work: Kimi K3 for raw capability and GLM-5.3 on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, Kimi K3 or GLM-5.3?
GLM-5.3 is cheaper at $1.40 input / $4.40 output per million tokens (official Z.AI API price). Kimi K3 costs $3.00 input / $15.00 output per million tokens (official Moonshot AI API price). At a typical mix of three input tokens to one output token, that is $2.15 per million tokens for GLM-5.3 versus $6.00 for Kimi K3 (2.8× as much).
Which scores higher on benchmarks?
Kimi K3 scores higher on the Capabilities Index (ECI): Kimi K3 158.3 (#20 of 165) and GLM-5.3 156.2 (#33 of 165). The confidence ranges of the top two overlap (155.8–161.4 vs 154.3–158.5), so treat the gap as small. On individual benchmarks: GPQA Diamond — Kimi K3 93.1%, GLM-5.3 90.9%; FrontierMath Tiers 1–3 — Kimi K3 72.2%, GLM-5.3 68.8%; OTIS Mock AIME 2024–2025 — Kimi K3 97.2%, GLM-5.3 91.1%; SimpleQA Verified — Kimi K3 50.6%, GLM-5.3 41.0%.
Which is better for coding?
There are no published SWE-bench Verified results for Kimi K3 and GLM-5.3 yet, so there is no like-for-like coding score. On overall capability, Kimi K3 leads, which tends to carry over to coding, but test on your own codebase. Both support tool calling for agent workflows.
Which has the bigger context window?
Kimi K3 has the largest context window at 1,048,576 tokens, against 1,000,000 for GLM-5.3. Maximum output per response: Kimi K3 up to 131,072, GLM-5.3 up to 131,072 tokens.
Which can read images, PDFs, audio or video?
Kimi K3 accepts text, images and video; GLM-5.3 accepts text. Kimi K3 handles the widest range of inputs.
Are any of these open source?
Yes, both publish their weights, so you can self-host them.
Which is newer?
GLM-5.3 is the newest, released Aug 14, 2026. Kimi K3 came out Jul 16, 2026.
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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