Muse Spark 1.2 vs Qwen3.8 Max vs GLM-5.3
Too close to call on our weighted score (Muse Spark 1.2 72, Qwen3.8 Max 69, GLM-5.3 64). The right pick depends on what you value most.
MetaMuse Spark 1.2
72/100- ECI155.0
- Price$1.25 / $4.25
- Context1.05M
Alibaba (Qwen)Qwen3.8 Max
69/100- ECI156.6
- Price$2.00 / $6.00
- Context1M
Z.ai (Zhipu)GLM-5.3
64/100- ECI155.8
- Price$1.40 / $4.40
- Context1M
Too close to call
It is close. Our weighted score puts them within 3 points (Muse Spark 1.2 72/100, Qwen3.8 Max 69/100, GLM-5.3 64/100), so choose by what matters most for your work: Qwen3.8 Max for raw capability, Muse Spark 1.2 on price and Muse Spark 1.2 for long inputs. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityQwen3.8 MaxCapabilities Index (ECI): Qwen3.8 Max 156.6 · GLM-5.3 155.8 · Muse Spark 1.2 155.0
- Lowest priceMuse Spark 1.2Muse Spark 1.2 $2.00 · GLM-5.3 $2.15 · Qwen3.8 Max $3.00 per 1M tokens (3:1 blend)
- Longest contextMuse Spark 1.2Muse Spark 1.2 1,048,576 · Qwen3.8 Max 1,000,000 · GLM-5.3 1,000,000 tokens
- Widest inputsMuse Spark 1.2Muse Spark 1.2: Text, Images, PDFs, Audio, Video · Qwen3.8 Max: Text, Images, PDFs, Video · GLM-5.3: Text
- Self-hostingGLM-5.3Publishes downloadable weights
| Measure | Weight | Muse Spark 1.2 | Qwen3.8 Max | GLM-5.3 |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 84 | 86 | 85 |
| Price | 25% | 36 | 27 | 34 |
| Inputs & features | 15% | 100 | 90 | 45 |
| Context window | 10% | 61 | 60 | 60 |
| Overall | 100% | 72/100 | 69/100 | 64/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Muse Spark 1.2Meta | Qwen3.8 MaxAlibaba (Qwen) | GLM-5.3Z.ai (Zhipu) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 155.0 | 156.6 (best) | 155.8 |
| ECI rank | #30 of 148 | #20 of 148 (best) | #24 of 148 |
| GPQA DiamondGraduate-level science questions | — | 92.7% (best) | 90.9% |
| FrontierMath Tiers 1–3Research-level mathematics | — | 74.7% (best) | 68.8% |
| OTIS Mock AIME 2024–2025Competition mathematics | — | 99.4% (best) | 91.1% |
| SimpleQA VerifiedShort factual questions | 60.3% (best) | 45.8% | 41.0% |
| Price per million tokens | |||
| Input | $1.25 (best) | $2.00 | $1.40 |
| Output | $4.25 (best) | $6.00 | $4.40 |
| Cached input | $0.15 (best) | $0.25 | $0.26 |
| Blended (3:1) | $2.00 (best) | $3.00 | $2.15 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Meta API | Official Alibaba API | Official Z.AI API |
| Limits | |||
| Context window | 1,048,576 tokens (best) | 1,000,000 tokens | 1,000,000 tokens |
| Max output | 131,072 tokens | 131,072 tokens | 131,072 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | Yes | No |
| PDFs | Yes | Yes | No |
| Audio | Yes | No | No |
| Video | Yes | Yes | No |
| Reasoning | Yesminimal · low · medium · high · xhigh | Yeslow · medium · xhigh | Yeslow · high · max |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | Yes | Yes |
| Availability | |||
| Weights | Proprietary | Proprietary | Open |
| API model ID | muse-spark-1.2 | qwen3.8-max | glm-5.3 |
| API providers | 15 | 25 | 62 (best) |
| Released | Aug 5, 2026 | Aug 3, 2026 | Aug 14, 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.
- Muse Spark 1.2$21.00
- Qwen3.8 Max$32.00
- GLM-5.3$22.80
Which should you choose?
Which is better: Muse Spark 1.2, Qwen3.8 Max or GLM-5.3?
It is close. Our weighted score puts them within 3 points (Muse Spark 1.2 72/100, Qwen3.8 Max 69/100, GLM-5.3 64/100), so choose by what matters most for your work: Qwen3.8 Max for raw capability, Muse Spark 1.2 on price and Muse Spark 1.2 for long inputs. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, Muse Spark 1.2, Qwen3.8 Max or GLM-5.3?
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 costs $2.00 input / $6.00 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 $3.00 for Qwen3.8 Max (1.5× as much).
Which scores higher on benchmarks?
Qwen3.8 Max scores higher on the Capabilities Index (ECI): Qwen3.8 Max 156.6 (#20 of 148), GLM-5.3 155.8 (#24 of 148) and Muse Spark 1.2 155.0 (#30 of 148). The confidence ranges of the top two overlap (154.5–158.8 vs 153.7–158.3), so treat the gap as small. On individual benchmarks: SimpleQA Verified — Muse Spark 1.2 60.3%, Qwen3.8 Max 45.8%, GLM-5.3 41.0%.
Which is better for coding?
There are no published SWE-bench Verified results for Muse Spark 1.2, Qwen3.8 Max and GLM-5.3 yet, so there is no like-for-like coding score. On overall capability, Qwen3.8 Max 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 Qwen3.8 Max and 1,000,000 for GLM-5.3. Maximum output per response: Muse Spark 1.2 up to 131,072, Qwen3.8 Max up to 131,072, GLM-5.3 up to 131,072 tokens.
Which can read images, PDFs, audio or video?
Muse Spark 1.2 accepts text, images, PDFs, audio and video; Qwen3.8 Max accepts text, images, PDFs and video; GLM-5.3 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 Qwen3.8 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; Qwen3.8 Max came out Aug 3, 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.
