Muse Spark 1.2 vs Muse Spark 1.3 vs GLM-5.3
Too close to call on our weighted score (Muse Spark 1.3 73, Muse Spark 1.2 72, 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
- Meta
Muse Spark 1.3
73/100- ECI156.9
- Price$1.25 / $4.25
- Context1.05M
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 1 points (Muse Spark 1.3 73/100, Muse Spark 1.2 72/100, GLM-5.3 64/100), so choose by what matters most for your work: Muse Spark 1.3 for raw capability and Muse Spark 1.2 on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityMuse Spark 1.3Capabilities Index (ECI): Muse Spark 1.3 156.9 · GLM-5.3 155.8 · Muse Spark 1.2 155.0
- Lowest priceMuse Spark 1.2 and Muse Spark 1.3Muse Spark 1.2 $2.00 · Muse Spark 1.3 $2.00 · GLM-5.3 $2.15 per 1M tokens (3:1 blend)
- Longest contextMuse Spark 1.2 and Muse Spark 1.3Muse Spark 1.2 1,048,576 · Muse Spark 1.3 1,048,576 · GLM-5.3 1,000,000 tokens
- Widest inputsMuse Spark 1.2 and Muse Spark 1.3Muse Spark 1.2: Text, Images, PDFs, Audio, Video · Muse Spark 1.3: Text, Images, PDFs, Audio, Video · GLM-5.3: Text
- Self-hostingGLM-5.3Publishes downloadable weights
| Measure | Weight | Muse Spark 1.2 | Muse Spark 1.3 | GLM-5.3 |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 84 | 87 | 85 |
| Price | 25% | 36 | 36 | 34 |
| Inputs & features | 15% | 100 | 100 | 45 |
| Context window | 10% | 61 | 61 | 60 |
| Overall | 100% | 72/100 | 73/100 | 64/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Muse Spark 1.2Meta | Muse Spark 1.3Meta | GLM-5.3Z.ai (Zhipu) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 155.0 | 156.9 (best) | 155.8 |
| ECI rank | #30 of 148 | #17 of 148 (best) | #24 of 148 |
| GPQA DiamondGraduate-level science questions | — | — | 90.9% |
| FrontierMath Tiers 1–3Research-level mathematics | — | 74.4% (best) | 68.8% |
| OTIS Mock AIME 2024–2025Competition mathematics | — | 99.2% (best) | 91.1% |
| SimpleQA VerifiedShort factual questions | 60.3% (best) | — | 41.0% |
| Price per million tokens | |||
| Input | $1.25 (best) | $1.25 (best) | $1.40 |
| Output | $4.25 (best) | $4.25 (best) | $4.40 |
| Cached input | $0.15 (best) | $0.15 (best) | $0.26 |
| Blended (3:1) | $2.00 (best) | $2.00 (best) | $2.15 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Meta API | Official Meta API | Official Z.AI API |
| Limits | |||
| Context window | 1,048,576 tokens (best) | 1,048,576 tokens (best) | 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 | Yes | No |
| Video | Yes | Yes | No |
| Reasoning | Yesminimal · low · medium · high · xhigh | Yesminimal · low · medium · high · xhigh · max | 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 | muse-spark-1.3 | glm-5.3 |
| API providers | 15 | 12 | 62 (best) |
| Released | Aug 5, 2026 | Sep 2, 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
- Muse Spark 1.3$21.00
- GLM-5.3$22.80
Which should you choose?
Which is better: Muse Spark 1.2, Muse Spark 1.3 or GLM-5.3?
It is close. Our weighted score puts them within 1 points (Muse Spark 1.3 73/100, Muse Spark 1.2 72/100, GLM-5.3 64/100), so choose by what matters most for your work: Muse Spark 1.3 for raw capability and Muse Spark 1.2 on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, Muse Spark 1.2, Muse Spark 1.3 or GLM-5.3?
Muse Spark 1.2 is cheaper at $1.25 input / $4.25 output per million tokens (official Meta API price). Muse Spark 1.3 costs $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). 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 $2.00 for Muse Spark 1.3 (1× as much) and $2.15 for GLM-5.3 (1.1× as much).
Which scores higher on benchmarks?
Muse Spark 1.3 scores higher on the Capabilities Index (ECI): Muse Spark 1.3 156.9 (#17 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 (154.7–159.6 vs 153.7–158.3), so treat the gap as small.
Which is better for coding?
There are no published SWE-bench Verified results for Muse Spark 1.2, Muse Spark 1.3 and GLM-5.3 yet, so there is no like-for-like coding score. On overall capability, Muse Spark 1.3 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 and Muse Spark 1.3 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: Muse Spark 1.2 up to 131,072, Muse Spark 1.3 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; Muse Spark 1.3 accepts text, images, PDFs, audio 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 Muse Spark 1.3 is proprietary.
Which is newer?
Muse Spark 1.3 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.
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