GLM-5.3 vs Muse Spark 1.2 vs Qwen3.7 Max
Muse Spark 1.2 comes out ahead, 72 to 64 and 58 on our weighted score, and it is the cheaper option too.
Z.ai (Zhipu)GLM-5.3
64/100- ECI155.8
- Price$1.40 / $4.40
- Context1M
- Our pick
MetaMuse Spark 1.2
72/100- ECI155.0
- Price$1.25 / $4.25
- Context1.05M
Alibaba (Qwen)Qwen3.7 Max
58/100- ECI153.7
- Price$2.50 / $7.50
- Context1M
Muse Spark 1.2 is our pick
Muse Spark 1.2 is the better all-round choice, scoring 72/100 against GLM-5.3 (64) and Qwen3.7 Max (58). It leads on inputs & features. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityGLM-5.3Capabilities Index (ECI): GLM-5.3 155.8 · Muse Spark 1.2 155.0 · Qwen3.7 Max 153.7
- Lowest priceMuse Spark 1.2Muse Spark 1.2 $2.00 · GLM-5.3 $2.15 · Qwen3.7 Max $3.75 per 1M tokens (3:1 blend)
- Longest contextMuse Spark 1.2Muse Spark 1.2 1,048,576 · GLM-5.3 1,000,000 · Qwen3.7 Max 1,000,000 tokens
- Widest inputsMuse Spark 1.2GLM-5.3: Text · Muse Spark 1.2: Text, Images, PDFs, Audio, Video · Qwen3.7 Max: Text
- Self-hostingGLM-5.3Publishes downloadable weights
| Measure | Weight | GLM-5.3 | Muse Spark 1.2 | Qwen3.7 Max |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 85 | 84 | 83 |
| Price | 25% | 34 | 36 | 23 |
| Inputs & features | 15% | 45 | 100 | 35 |
| Context window | 10% | 60 | 61 | 60 |
| Overall | 100% | 64/100 | 72/100 | 58/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | GLM-5.3Z.ai (Zhipu) | Muse Spark 1.2Meta | Qwen3.7 MaxAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 155.8 (best) | 155.0 | 153.7 |
| ECI rank | #24 of 148 (best) | #30 of 148 | #37 of 148 |
| GPQA DiamondGraduate-level science questions | 90.9% | — | 90.9% |
| FrontierMath Tiers 1–3Research-level mathematics | 68.8% (best) | — | 64.6% |
| OTIS Mock AIME 2024–2025Competition mathematics | 91.1% | — | 95.6% (best) |
| SWE-bench VerifiedFixing real GitHub issues | — | — | 77.3% |
| SimpleQA VerifiedShort factual questions | 41.0% | 60.3% (best) | 55.8% |
| Price per million tokens | |||
| Input | $1.40 | $1.25 (best) | $2.50 |
| Output | $4.40 | $4.25 (best) | $7.50 |
| Cached input | $0.26 | $0.15 (best) | $0.50 |
| Blended (3:1) | $2.15 | $2.00 (best) | $3.75 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Z.AI API | Official Meta API | Official Alibaba API |
| Limits | |||
| Context window | 1,000,000 tokens | 1,048,576 tokens (best) | 1,000,000 tokens |
| Max output | 131,072 tokens (best) | 131,072 tokens (best) | 65,536 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | No |
| PDFs | No | Yes | No |
| Audio | No | Yes | No |
| Video | No | Yes | No |
| Reasoning | Yeslow · high · max | Yesminimal · low · medium · high · xhigh | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | Yes | No |
| Availability | |||
| Weights | Open | Proprietary | Proprietary |
| API model ID | glm-5.3 | muse-spark-1.2 | qwen3.7-max |
| API providers | 62 (best) | 15 | 26 |
| Released | Aug 14, 2026 | Aug 5, 2026 | May 21, 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
- Muse Spark 1.2$21.00
- Qwen3.7 Max$40.00
Which should you choose?
Which is better: GLM-5.3, Muse Spark 1.2 or Qwen3.7 Max?
Muse Spark 1.2 is the better all-round choice, scoring 72/100 against GLM-5.3 (64) and Qwen3.7 Max (58). It leads on inputs & features. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, GLM-5.3, Muse Spark 1.2 or Qwen3.7 Max?
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); Qwen3.7 Max costs $2.50 input / $7.50 output per million tokens (official Alibaba 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 $3.75 for Qwen3.7 Max (1.9× as much).
Which scores higher on benchmarks?
GLM-5.3 scores higher on the Capabilities Index (ECI): GLM-5.3 155.8 (#24 of 148), Muse Spark 1.2 155.0 (#30 of 148) and Qwen3.7 Max 153.7 (#37 of 148). The confidence ranges of the top two overlap (153.7–158.3 vs 152.8–157.5), so treat the gap as small. On individual benchmarks: SimpleQA Verified — Muse Spark 1.2 60.3%, Qwen3.7 Max 55.8%, GLM-5.3 41.0%.
Which is better for coding?
There are no published SWE-bench Verified results for GLM-5.3 and Muse Spark 1.2 yet, so there is no like-for-like coding score. On overall capability, GLM-5.3 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 1.2 has the largest context window at 1,048,576 tokens, against 1,000,000 for GLM-5.3 and 1,000,000 for Qwen3.7 Max. Maximum output per response: GLM-5.3 up to 131,072, Muse Spark 1.2 up to 131,072, Qwen3.7 Max up to 65,536 tokens.
Which can read images, PDFs, audio or video?
GLM-5.3 accepts text; Muse Spark 1.2 accepts text, images, PDFs, audio and video; Qwen3.7 Max accepts text. Muse Spark 1.2 handles the widest range of inputs.
Are any of these open source?
GLM-5.3 publishes its weights and can be self-hosted; Muse Spark 1.2 and Qwen3.7 Max is proprietary.
Which is newer?
GLM-5.3 is the newest, released Aug 14, 2026. Muse Spark 1.2 came out Aug 5, 2026; Qwen3.7 Max came out May 21, 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 Oct 4, 2026.
