GLM-5.2 vs Muse Spark 1.1 vs Kimi K2.6
Muse Spark 1.1 comes out ahead, 70 to 65 and 61 on our weighted score, though Kimi K2.6 is 14% cheaper per token.
Z.ai (Zhipu)GLM-5.2
61/100- ECI151.8
- Price$1.40 / $4.40
- Context1M
- Our pick
MetaMuse Spark 1.1
70/100- ECI154.3
- Price$1.25 / $4.25
- Context1.05M
Moonshot AIKimi K2.6
65/100- ECI151.1
- Price$0.95 / $4.00
- Context262K
Muse Spark 1.1 is our pick
Muse Spark 1.1 is the better all-round choice, scoring 70/100 against Kimi K2.6 (65) and GLM-5.2 (61). It leads on capability and inputs & features. Kimi K2.6 wins on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityMuse Spark 1.1Capabilities Index (ECI): Muse Spark 1.1 154.3 · GLM-5.2 151.8 · Kimi K2.6 151.1
- Lowest priceKimi K2.6Kimi K2.6 $1.71 · Muse Spark 1.1 $2.00 · GLM-5.2 $2.15 per 1M tokens (3:1 blend)
- Longest contextMuse Spark 1.1Muse Spark 1.1 1,048,576 · GLM-5.2 1,000,000 · Kimi K2.6 262,144 tokens
- Widest inputsMuse Spark 1.1GLM-5.2: Text · Muse Spark 1.1: Text, Images, PDFs, Video · Kimi K2.6: Text, Images, Video
- Self-hostingGLM-5.2 and Kimi K2.6Publishes downloadable weights
| Measure | Weight | GLM-5.2 | Muse Spark 1.1 | Kimi K2.6 |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 80 | 84 | 79 |
| Price | 25% | 34 | 36 | 39 |
| Inputs & features | 15% | 45 | 90 | 80 |
| Context window | 10% | 60 | 61 | 37 |
| Overall | 100% | 61/100 | 70/100 | 65/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | GLM-5.2Z.ai (Zhipu) | Muse Spark 1.1Meta | Kimi K2.6Moonshot AI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 151.8 | 154.3 (best) | 151.1 |
| ECI rank | #44 of 148 | #35 of 148 (best) | #45 of 148 |
| GPQA DiamondGraduate-level science questions | 91.9% (best) | — | 90.8% |
| FrontierMath Tiers 1–3Research-level mathematics | 59.2% (best) | — | 57.2% |
| OTIS Mock AIME 2024–2025Competition mathematics | 86.4% | — | 96.1% (best) |
| SWE-bench VerifiedFixing real GitHub issues | 78.7% (best) | — | 76.7% |
| SimpleQA VerifiedShort factual questions | 34.2% | 57.8% (best) | 34.9% |
| Price per million tokens | |||
| Input | $1.40 | $1.25 | $0.95 (best) |
| Output | $4.40 | $4.25 | $4.00 (best) |
| Cached input | $0.26 | $0.15 (best) | $0.16 |
| Blended (3:1) | $2.15 | $2.00 | $1.71 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Z.AI API | Official Meta API | Official Moonshot AI API |
| Limits | |||
| Context window | 1,000,000 tokens | 1,048,576 tokens (best) | 262,144 tokens |
| Max output | 131,072 tokens | 131,072 tokens | 262,144 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | Yes |
| PDFs | No | Yes | No |
| Audio | No | No | No |
| Video | No | Yes | Yes |
| Reasoning | Yeshigh · max | Yesminimal · low · medium · high · xhigh | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | Yes | Yes |
| Availability | |||
| Weights | Open | Proprietary | Open |
| API model ID | glm-5.2 | muse-spark-1.1 | kimi-k2.6 |
| API providers | 80 (best) | 13 | 46 |
| Released | Jun 13, 2026 | Jul 9, 2026 | Apr 21, 2026 |
| Knowledge cutoff | — | — | Jan 2025 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- GLM-5.2$22.80
- Muse Spark 1.1$21.00
- Kimi K2.6$17.50
Which should you choose?
Which is better: GLM-5.2, Muse Spark 1.1 or Kimi K2.6?
Muse Spark 1.1 is the better all-round choice, scoring 70/100 against Kimi K2.6 (65) and GLM-5.2 (61). It leads on capability and inputs & features. Kimi K2.6 wins on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, GLM-5.2, Muse Spark 1.1 or Kimi K2.6?
Kimi K2.6 is cheaper at $0.95 input / $4.00 output per million tokens (official Moonshot AI API price). Muse Spark 1.1 costs $1.25 input / $4.25 output per million tokens (official Meta API price); GLM-5.2 costs $1.40 input / $4.40 output per million tokens (official Z.AI API price). At a typical mix of three input tokens to one output token, that is $1.71 per million tokens for Kimi K2.6 versus $2.00 for Muse Spark 1.1 (1.2× as much) and $2.15 for GLM-5.2 (1.3× as much).
Which scores higher on benchmarks?
Muse Spark 1.1 scores higher on the Capabilities Index (ECI): Muse Spark 1.1 154.3 (#35 of 148), GLM-5.2 151.8 (#44 of 148) and Kimi K2.6 151.1 (#45 of 148). The confidence ranges of the top two overlap (152.2–157.1 vs 149.8–154.0), so treat the gap as small. On individual benchmarks: SimpleQA Verified — Muse Spark 1.1 57.8%, Kimi K2.6 34.9%, GLM-5.2 34.2%.
Which is better for coding?
There are no published SWE-bench Verified results for Muse Spark 1.1 yet, so there is no like-for-like coding score. On overall capability, Muse Spark 1.1 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?
Muse Spark 1.1 has the largest context window at 1,048,576 tokens, against 1,000,000 for GLM-5.2 and 262,144 for Kimi K2.6. Maximum output per response: GLM-5.2 up to 131,072, Muse Spark 1.1 up to 131,072, Kimi K2.6 up to 262,144 tokens.
Which can read images, PDFs, audio or video?
GLM-5.2 accepts text; Muse Spark 1.1 accepts text, images, PDFs and video; Kimi K2.6 accepts text, images and video. Muse Spark 1.1 handles the widest range of inputs.
Are any of these open source?
GLM-5.2 and Kimi K2.6 publishes its weights and can be self-hosted; Muse Spark 1.1 is proprietary.
Which is newer?
Muse Spark 1.1 is the newest, released Jul 9, 2026. GLM-5.2 came out Jun 13, 2026; Kimi K2.6 came out Apr 21, 2026. Knowledge cutoff: Kimi K2.6 Jan 2025.
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.
