GLM-5.3 vs Grok 4.7 vs Muse Spark 1.2
Muse Spark 1.2 comes out ahead, 60 to 52 and 42 on our weighted score, and it is the cheaper option too.
Z.ai (Zhipu)GLM-5.3
42/100- ECI155.8
- Price$1.40 / $4.40
- Context1M
xAIGrok 4.7
52/100- ECI—
- Price$2.00 / $6.00
- Context500K
- Our pick
MetaMuse Spark 1.2
60/100- 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 60/100 against Grok 4.7 (52) and GLM-5.3 (42). It leads on capability and inputs & features. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%. Capability uses the average of shared benchmarks (SimpleQA Verified), because Grok 4.7 has no Capabilities Index score yet.
- CapabilityMuse Spark 1.2Shared benchmarks: Muse Spark 1.2 60.3% · Grok 4.7 56.0% · GLM-5.3 41.0%
- Lowest priceMuse Spark 1.2Muse Spark 1.2 $2.00 · GLM-5.3 $2.15 · Grok 4.7 $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 · Grok 4.7 500,000 tokens
- Widest inputsMuse Spark 1.2GLM-5.3: Text · Grok 4.7: Text, Images, PDFs · Muse Spark 1.2: Text, Images, PDFs, Audio, Video
- Self-hostingGLM-5.3Publishes downloadable weights
| Measure | Weight | GLM-5.3 | Grok 4.7 | Muse Spark 1.2 |
|---|---|---|---|---|
| CapabilityShared benchmarks | 50% | 41 | 56 | 60 |
| Price | 25% | 34 | 27 | 36 |
| Inputs & features | 15% | 45 | 80 | 100 |
| Context window | 10% | 60 | 48 | 61 |
| Overall | 100% | 42/100 | 52/100 | 60/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | GLM-5.3Z.ai (Zhipu) | Grok 4.7xAI | Muse Spark 1.2Meta |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 155.8 (best) | — | 155.0 |
| ECI rank | #24 of 148 (best) | — | #30 of 148 |
| GPQA DiamondGraduate-level science questions | 90.9% | 92.7% (best) | — |
| FrontierMath Tiers 1–3Research-level mathematics | 68.8% (best) | 53.0% | — |
| OTIS Mock AIME 2024–2025Competition mathematics | 91.1% | 98.1% (best) | — |
| SimpleQA VerifiedShort factual questions | 41.0% | 56.0% | 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 | $0.50 | $0.15 (best) |
| Blended (3:1) | $2.15 | $3.00 | $2.00 (best) |
| Long-context rate | Same rate | Over 200K: $4.00 / $12.00 | Same rate |
| Price source | Official Z.AI API | Official xAI API | Official Meta API |
| Limits | |||
| Context window | 1,000,000 tokens | 500,000 tokens | 1,048,576 tokens (best) |
| Max output | 131,072 tokens | 500,000 tokens (best) | 131,072 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | Yes |
| PDFs | No | Yes | Yes |
| Audio | No | No | Yes |
| Video | No | No | Yes |
| Reasoning | Yeslow · high · max | Yeslow · medium · high · xhigh | Yesminimal · low · medium · high · xhigh |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | Yes | Yes |
| Availability | |||
| Weights | Open | Proprietary | Proprietary |
| API model ID | glm-5.3 | grok-4.7 | muse-spark-1.2 |
| API providers | 62 (best) | 17 | 15 |
| Released | Aug 14, 2026 | Sep 21, 2026 | Aug 5, 2026 |
| Knowledge cutoff | — | May 2026 | — |
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
- Grok 4.7$32.00
- Muse Spark 1.2$21.00
Which should you choose?
Which is better: GLM-5.3, Grok 4.7 or Muse Spark 1.2?
Muse Spark 1.2 is the better all-round choice, scoring 60/100 against Grok 4.7 (52) and GLM-5.3 (42). It leads on capability and inputs & features. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%. Capability uses the average of shared benchmarks (SimpleQA Verified), because Grok 4.7 has no Capabilities Index score yet.
Which is cheaper, GLM-5.3, Grok 4.7 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); Grok 4.7 costs $2.00 input / $6.00 output per million tokens (official xAI 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 Grok 4.7 (1.5× as much).
Which scores higher on benchmarks?
Not every model here has a Capabilities Index score, so we compare the average of shared benchmarks (SimpleQA Verified): Muse Spark 1.2 60.3%, Grok 4.7 56.0% and GLM-5.3 41.0%. On individual benchmarks: SimpleQA Verified — Muse Spark 1.2 60.3%, Grok 4.7 56.0%, GLM-5.3 41.0%.
Which is better for coding?
There are no published SWE-bench Verified results for GLM-5.3, Grok 4.7 and Muse Spark 1.2 yet, so there is no like-for-like coding score. On overall capability, Muse Spark 1.2 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 500,000 for Grok 4.7. Maximum output per response: GLM-5.3 up to 131,072, Grok 4.7 up to 500,000, Muse Spark 1.2 up to 131,072 tokens.
Which can read images, PDFs, audio or video?
GLM-5.3 accepts text; Grok 4.7 accepts text, images and PDFs; 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; Grok 4.7 and Muse Spark 1.2 is proprietary.
Which is newer?
Grok 4.7 is the newest, released Sep 21, 2026. GLM-5.3 came out Aug 14, 2026; Muse Spark 1.2 came out Aug 5, 2026. Knowledge cutoff: Grok 4.7 May 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.
