GLM-5.3 vs GPT-6.1 Sol vs Muse Spark 1.2
Too close to call on our weighted score (GPT-6.1 Sol 72, Muse Spark 1.2 71, GLM-5.3 63). The right pick depends on what you value most.
Z.ai (Zhipu)
GLM-5.3
63/100- ECI156.2
- Price$1.40 / $4.40
- Context1M
OpenAI
GPT-6.1 Sol
72/100- ECI167.8
- Price$2.00 / $10.00
- Context1.05M
Meta
Muse Spark 1.2
71/100- ECI155.2
- Price$1.25 / $4.25
- Context1.05M
Too close to call
It is close. Our weighted score puts them within 1 points (GPT-6.1 Sol 72/100, Muse Spark 1.2 71/100, GLM-5.3 63/100), so choose by what matters most for your work: GPT-6.1 Sol for raw capability and Muse Spark 1.2 on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityGPT-6.1 SolCapabilities Index (ECI): GPT-6.1 Sol 167.8 · GLM-5.3 156.2 · Muse Spark 1.2 155.2
- Lowest priceMuse Spark 1.2Muse Spark 1.2 $2.00 · GLM-5.3 $2.15 · GPT-6.1 Sol $4.00 per 1M tokens (3:1 blend)
- Longest contextGPT-6.1 Sol and Muse Spark 1.2GPT-6.1 Sol 1,050,000 · Muse Spark 1.2 1,048,576 · GLM-5.3 1,000,000 tokens
- Widest inputsMuse Spark 1.2GLM-5.3: Text · GPT-6.1 Sol: Text, Images, PDFs · Muse Spark 1.2: Text, Images, PDFs, Audio, Video
- Self-hostingGLM-5.3Publishes downloadable weights
| Measure | Weight | GLM-5.3 | GPT-6.1 Sol | Muse Spark 1.2 |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 84 | 99 | 83 |
| Price | 25% | 33 | 20 | 34 |
| Inputs & features | 15% | 45 | 80 | 100 |
| Context window | 10% | 60 | 61 | 61 |
| Overall | 100% | 63/100 | 72/100 | 71/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | |||
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 156.2 | 167.8 (best) | 155.2 |
| ECI rank | #33 of 165 | #4 of 165 (best) | #38 of 165 |
| GPQA DiamondGraduate-level science questions | 90.9% | 95.4% (best) | — |
| FrontierMath Tiers 1–3Research-level mathematics | 68.8% | 93.7% (best) | — |
| OTIS Mock AIME 2024–2025Competition mathematics | 91.1% | 100% (best) | — |
| SimpleQA VerifiedShort factual questions | 41.0% | 73.9% (best) | 60.3% |
| Price per million tokens | |||
| Input | $1.40 | $2.00 | $1.25 (best) |
| Output | $4.40 | $10.00 | $4.25 (best) |
| Cached input | $0.26 | $0.10 (best) | $0.15 |
| Blended (3:1) | $2.15 | $4.00 | $2.00 (best) |
| Long-context rate | Same rate | Over 272K: $4.00 / $15.00 | Same rate |
| Price source | Official Z.AI API | Official OpenAI API | Official Meta API |
| Limits | |||
| Context window | 1,000,000 tokens | 1,050,000 tokens (best) | 1,048,576 tokens |
| Max output | 131,072 tokens (best) | 128,000 tokens | 131,072 tokens (best) |
| 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 · max | 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 | gpt-6.1-sol | muse-spark-1.2 |
| API providers | 83 (best) | 33 | 18 |
| Released | Aug 14, 2026 | Sep 29, 2026 | Aug 5, 2026 |
| Knowledge cutoff | — | Apr 30, 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
GPT-6.1 Sol$40.00
Muse Spark 1.2$21.00
Which should you choose?
Which is better: GLM-5.3, GPT-6.1 Sol or Muse Spark 1.2?
It is close. Our weighted score puts them within 1 points (GPT-6.1 Sol 72/100, Muse Spark 1.2 71/100, GLM-5.3 63/100), so choose by what matters most for your work: GPT-6.1 Sol 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, GLM-5.3, GPT-6.1 Sol 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); GPT-6.1 Sol costs $2.00 input / $10.00 output per million tokens (official OpenAI 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 $4.00 for GPT-6.1 Sol (2× as much).
Which scores higher on benchmarks?
GPT-6.1 Sol scores higher on the Capabilities Index (ECI): GPT-6.1 Sol 167.8 (#4 of 165), GLM-5.3 156.2 (#33 of 165) and Muse Spark 1.2 155.2 (#38 of 165). Their confidence ranges do not overlap (164.3–172.5 vs 154.3–158.5), so the gap is a real one. On individual benchmarks: SimpleQA Verified — GPT-6.1 Sol 73.9%, Muse Spark 1.2 60.3%, GLM-5.3 41.0%.
Which is better for coding?
There are no published SWE-bench Verified results for GLM-5.3, GPT-6.1 Sol and Muse Spark 1.2 yet, so there is no like-for-like coding score. On overall capability, GPT-6.1 Sol 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?
GPT-6.1 Sol and Muse Spark 1.2 have the largest context windows (1,050,000 and 1,048,576 tokens), against 1,000,000 for GLM-5.3. Maximum output per response: GLM-5.3 up to 131,072, GPT-6.1 Sol up to 128,000, Muse Spark 1.2 up to 131,072 tokens.
Which can read images, PDFs, audio or video?
GLM-5.3 accepts text; GPT-6.1 Sol 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; GPT-6.1 Sol and Muse Spark 1.2 is proprietary.
Which is newer?
GPT-6.1 Sol is the newest, released Sep 29, 2026. GLM-5.3 came out Aug 14, 2026; Muse Spark 1.2 came out Aug 5, 2026. Knowledge cutoff: GPT-6.1 Sol Apr 30, 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 Nov 2, 2026.