GLM-5.3 vs Kimi K3 vs Muse Spark 1.2
Muse Spark 1.2 comes out ahead, 71 to 65 and 63 on our weighted score, and it is the cheaper option too.
Z.ai (Zhipu)GLM-5.3
63/100- ECI156.2
- Price$1.40 / $4.40
- Context1M
Moonshot AIKimi K3
65/100- ECI158.3
- Price$3.00 / $15.00
- Context1.05M
- Our pick
MetaMuse Spark 1.2
71/100- ECI155.2
- Price$1.25 / $4.25
- Context1.05M
Muse Spark 1.2 is our pick
Muse Spark 1.2 is the better all-round choice, scoring 71/100 against Kimi K3 (65) and GLM-5.3 (63). It leads on inputs & features. Kimi K3 wins on capability. 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 · Muse Spark 1.2 155.2
- Lowest priceMuse Spark 1.2Muse Spark 1.2 $2.00 · GLM-5.3 $2.15 · Kimi K3 $6.00 per 1M tokens (3:1 blend)
- Longest contextKimi K3 and Muse Spark 1.2Kimi K3 1,048,576 · Muse Spark 1.2 1,048,576 · GLM-5.3 1,000,000 tokens
- Widest inputsMuse Spark 1.2GLM-5.3: Text · Kimi K3: Text, Images, Video · Muse Spark 1.2: Text, Images, PDFs, Audio, Video
- Self-hostingGLM-5.3 and Kimi K3Publishes downloadable weights
| Measure | Weight | GLM-5.3 | Kimi K3 | Muse Spark 1.2 |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 84 | 87 | 83 |
| Price | 25% | 33 | 12 | 34 |
| Inputs & features | 15% | 45 | 80 | 100 |
| Context window | 10% | 60 | 61 | 61 |
| Overall | 100% | 63/100 | 65/100 | 71/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | GLM-5.3Z.ai (Zhipu) | Kimi K3Moonshot AI | Muse Spark 1.2Meta |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 156.2 | 158.3 (best) | 155.2 |
| ECI rank | #33 of 165 | #20 of 165 (best) | #38 of 165 |
| GPQA DiamondGraduate-level science questions | 90.9% | 93.1% (best) | — |
| FrontierMath Tiers 1–3Research-level mathematics | 68.8% | 72.2% (best) | — |
| OTIS Mock AIME 2024–2025Competition mathematics | 91.1% | 97.2% (best) | — |
| SimpleQA VerifiedShort factual questions | 41.0% | 50.6% | 60.3% (best) |
| Price per million tokens | |||
| Input | $1.40 | $3.00 | $1.25 (best) |
| Output | $4.40 | $15.00 | $4.25 (best) |
| Cached input | $0.26 | $0.30 | $0.15 (best) |
| Blended (3:1) | $2.15 | $6.00 | $2.00 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Z.AI API | Official Moonshot AI API | Official Meta API |
| Limits | |||
| Context window | 1,000,000 tokens | 1,048,576 tokens (best) | 1,048,576 tokens (best) |
| Max output | 131,072 tokens | 131,072 tokens | 131,072 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | Yes |
| PDFs | No | No | Yes |
| Audio | No | No | Yes |
| Video | No | Yes | Yes |
| Reasoning | Yeslow · high · max | Yeslow · high · max | Yesminimal · low · medium · high · xhigh |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | Yes | Yes |
| Availability | |||
| Weights | Open | Open | Proprietary |
| API model ID | glm-5.3 | kimi-k3 | muse-spark-1.2 |
| API providers | 83 (best) | 76 | 18 |
| Released | Aug 14, 2026 | Jul 16, 2026 | Aug 5, 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.
- GLM-5.3$22.80
- Kimi K3$60.00
- Muse Spark 1.2$21.00
Which should you choose?
Which is better: GLM-5.3, Kimi K3 or Muse Spark 1.2?
Muse Spark 1.2 is the better all-round choice, scoring 71/100 against Kimi K3 (65) and GLM-5.3 (63). It leads on inputs & features. Kimi K3 wins on capability. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, GLM-5.3, Kimi K3 or Muse Spark 1.2?
Muse Spark 1.2 is cheaper at $1.25 input / $4.25 output per million tokens (official Meta API price). GLM-5.3 costs $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 $2.00 per million tokens for Muse Spark 1.2 versus $2.15 for GLM-5.3 (1.1× as much) and $6.00 for Kimi K3 (3× as much).
Which scores higher on benchmarks?
Kimi K3 scores higher on the Capabilities Index (ECI): Kimi K3 158.3 (#20 of 165), GLM-5.3 156.2 (#33 of 165) and Muse Spark 1.2 155.2 (#38 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: SimpleQA Verified — Muse Spark 1.2 60.3%, Kimi K3 50.6%, GLM-5.3 41.0%.
Which is better for coding?
There are no published SWE-bench Verified results for GLM-5.3, Kimi K3 and Muse Spark 1.2 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. All three support tool calling for agent workflows.
Which has the bigger context window?
Kimi K3 and Muse Spark 1.2 have the largest context windows (1,048,576 and 1,048,576 tokens), against 1,000,000 for GLM-5.3. Maximum output per response: GLM-5.3 up to 131,072, Kimi K3 up to 131,072, Muse Spark 1.2 up to 131,072 tokens.
Which can read images, PDFs, audio or video?
GLM-5.3 accepts text; Kimi K3 accepts text, images and video; Muse Spark 1.2 accepts text, images, PDFs, audio and video. Muse Spark 1.2 handles the widest range of inputs.
Are any of these open source?
GLM-5.3 and Kimi K3 publishes its weights and can be self-hosted; Muse Spark 1.2 is proprietary.
Which is newer?
GLM-5.3 is the newest, released Aug 14, 2026. Muse Spark 1.2 came out Aug 5, 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.
