GLM-5.3 vs Qwen3.8 Max 0902 vs Muse Spark 1.2
Muse Spark 1.2 comes out ahead, 72 to 67 and 64 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
Alibaba (Qwen)Qwen3.8 Max 0902
67/100- ECI155.2
- Price$2.00 / $6.00
- Context1M
- Our pick
MetaMuse Spark 1.2
72/100- ECI155.0
- 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 72/100 against Qwen3.8 Max 0902 (67) and GLM-5.3 (64). 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 · Qwen3.8 Max 0902 155.2 · Muse Spark 1.2 155.0
- Lowest priceMuse Spark 1.2Muse Spark 1.2 $2.00 · GLM-5.3 $2.15 · Qwen3.8 Max 0902 $3.00 per 1M tokens (3:1 blend)
- Longest contextMuse Spark 1.2Muse Spark 1.2 1,048,576 · GLM-5.3 1,000,000 · Qwen3.8 Max 0902 1,000,000 tokens
- Widest inputsMuse Spark 1.2GLM-5.3: Text · Qwen3.8 Max 0902: Text, Images, PDFs, Video · Muse Spark 1.2: Text, Images, PDFs, Audio, Video
- Self-hostingGLM-5.3Publishes downloadable weights
| Measure | Weight | GLM-5.3 | Qwen3.8 Max 0902 | Muse Spark 1.2 |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 85 | 85 | 84 |
| Price | 25% | 34 | 27 | 36 |
| Inputs & features | 15% | 45 | 80 | 100 |
| Context window | 10% | 60 | 60 | 61 |
| Overall | 100% | 64/100 | 67/100 | 72/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | GLM-5.3Z.ai (Zhipu) | Qwen3.8 Max 0902Alibaba (Qwen) | Muse Spark 1.2Meta |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 155.8 (best) | 155.2 | 155.0 |
| ECI rank | #24 of 148 (best) | #28 of 148 | #30 of 148 |
| GPQA DiamondGraduate-level science questions | 90.9% | 92.3% (best) | — |
| FrontierMath Tiers 1–3Research-level mathematics | 68.8% (best) | 65.6% | — |
| OTIS Mock AIME 2024–2025Competition mathematics | 91.1% | 100% (best) | — |
| SimpleQA VerifiedShort factual questions | 41.0% | 47.3% | 60.3% (best) |
| Price per million tokens | |||
| Input | $1.40 | $2.00 | $1.25 (best) |
| Output | $4.40 | $6.00 | $4.25 (best) |
| Cached input | $0.26 | — | $0.15 (best) |
| Blended (3:1) | $2.15 | $3.00 | $2.00 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Z.AI API | Median of 6 providers | Official Meta API |
| Limits | |||
| Context window | 1,000,000 tokens | 1,000,000 tokens | 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 | Yes | Yes |
| Audio | No | No | Yes |
| Video | No | Yes | Yes |
| Reasoning | Yeslow · high · max | Yes | Yesminimal · low · medium · high · xhigh |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | No | Yes |
| Availability | |||
| Weights | Open | Proprietary | Proprietary |
| API model ID | glm-5.3 | — | muse-spark-1.2 |
| API providers | 62 (best) | 6 | 15 |
| Released | Aug 14, 2026 | Sep 2, 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
- Qwen3.8 Max 0902$32.00
- Muse Spark 1.2$21.00
Which should you choose?
Which is better: GLM-5.3, Qwen3.8 Max 0902 or Muse Spark 1.2?
Muse Spark 1.2 is the better all-round choice, scoring 72/100 against Qwen3.8 Max 0902 (67) and GLM-5.3 (64). It leads on inputs & features. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, GLM-5.3, Qwen3.8 Max 0902 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); Qwen3.8 Max 0902 costs $2.00 input / $6.00 output per million tokens (median across 6 API providers). 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 $3.00 for Qwen3.8 Max 0902 (1.5× 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), Qwen3.8 Max 0902 155.2 (#28 of 148) and Muse Spark 1.2 155.0 (#30 of 148). The confidence ranges of the top two overlap (153.7–158.3 vs 153.2–157.3), so treat the gap as small. On individual benchmarks: SimpleQA Verified — Muse Spark 1.2 60.3%, Qwen3.8 Max 0902 47.3%, GLM-5.3 41.0%.
Which is better for coding?
There are no published SWE-bench Verified results for GLM-5.3, Qwen3.8 Max 0902 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 Qwen3.8 Max 0902. Maximum output per response: GLM-5.3 up to 131,072, Qwen3.8 Max 0902 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; Qwen3.8 Max 0902 accepts text, images, PDFs 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 publishes its weights and can be self-hosted; Qwen3.8 Max 0902 and Muse Spark 1.2 is proprietary.
Which is newer?
Qwen3.8 Max 0902 is the newest, released Sep 2, 2026. GLM-5.3 came out Aug 14, 2026; Muse Spark 1.2 came out Aug 5, 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.
