GLM-5.3 vs Kimi K3 vs Muse Spark Open
Muse Spark Open comes out ahead, 68 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 Open
68/100- ECI155.1
- Price$1.25 / $4.25
- Context1.05M
Muse Spark Open is our pick
Muse Spark Open is the better all-round choice, scoring 68/100 against Kimi K3 (65) and GLM-5.3 (63). 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 Open 155.1
- Lowest priceMuse Spark OpenMuse Spark Open $2.00 · GLM-5.3 $2.15 · Kimi K3 $6.00 per 1M tokens (3:1 blend)
- Longest contextKimi K3 and Muse Spark OpenKimi K3 1,048,576 · Muse Spark Open 1,048,576 · GLM-5.3 1,000,000 tokens
- Widest inputsMuse Spark OpenGLM-5.3: Text · Kimi K3: Text, Images, Video · Muse Spark Open: Text, Images, Audio, Video
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | GLM-5.3 | Kimi K3 | Muse Spark Open |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 84 | 87 | 83 |
| Price | 25% | 33 | 12 | 34 |
| Inputs & features | 15% | 45 | 80 | 80 |
| Context window | 10% | 60 | 61 | 61 |
| Overall | 100% | 63/100 | 65/100 | 68/100 |
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 OpenMeta |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 156.2 | 158.3 (best) | 155.1 |
| ECI rank | #33 of 165 | #20 of 165 (best) | #39 of 165 |
| GPQA DiamondGraduate-level science questions | 90.9% | 93.1% (best) | 88.6% |
| FrontierMath Tiers 1–3Research-level mathematics | 68.8% | 72.2% (best) | 69.8% |
| OTIS Mock AIME 2024–2025Competition mathematics | 91.1% | 97.2% | 98.1% (best) |
| SimpleQA VerifiedShort factual questions | 41.0% | 50.6% | 56.6% (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 (best) | $0.30 | — |
| 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 | Median of 16 providers |
| 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 | No |
| Audio | No | No | Yes |
| Video | No | Yes | Yes |
| Reasoning | Yeslow · high · max | Yeslow · high · max | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | Yes | No |
| Availability | |||
| Weights | Open | Open | OpenMuse Model License |
| API model ID | glm-5.3 | kimi-k3 | — |
| API providers | 83 (best) | 76 | 16 |
| Released | Aug 14, 2026 | Jul 16, 2026 | Oct 15, 2026 |
| Knowledge cutoff | — | — | Jan 4, 2026 |
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 Open$21.00
Which should you choose?
Which is better: GLM-5.3, Kimi K3 or Muse Spark Open?
Muse Spark Open is the better all-round choice, scoring 68/100 against Kimi K3 (65) and GLM-5.3 (63). 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 Open?
Muse Spark Open is cheaper at $1.25 input / $4.25 output per million tokens (median across 16 API providers). 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 Open 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 Open 155.1 (#39 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%, Muse Spark Open 88.6%; FrontierMath Tiers 1–3 — Kimi K3 72.2%, Muse Spark Open 69.8%, GLM-5.3 68.8%; OTIS Mock AIME 2024–2025 — Muse Spark Open 98.1%, Kimi K3 97.2%, GLM-5.3 91.1%; SimpleQA Verified — Muse Spark Open 56.6%, 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 Open 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 Open 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 Open 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 Open accepts text, images, audio and video. Muse Spark Open handles the widest range of inputs.
Are any of these open source?
Yes, all three publish their weights (Muse Model License), so you can self-host them.
Which is newer?
Muse Spark Open is the newest, released Oct 15, 2026. GLM-5.3 came out Aug 14, 2026; Kimi K3 came out Jul 16, 2026. Knowledge cutoff: Muse Spark Open Jan 4, 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.
