Solar Pro 4 vs GLM-5.3 vs Muse Glimmer 30B
Too close to call on our weighted score (Muse Glimmer 30B 57, Solar Pro 4 55, GLM-5.3 43). The right pick depends on what you value most.
Upstage
Solar Pro 4
55/100- ECI—
- Price$0.30 / $1.20
- Context524K
Z.ai (Zhipu)GLM-5.3
43/100- ECI155.8
- Price$1.40 / $4.40
- Context1M
MetaMuse Glimmer 30B
57/100- ECI—
- Price$0.30 / $1.20
- Context131K
Too close to call
It is close. Our weighted score puts them within 3 points (Muse Glimmer 30B 57/100, Solar Pro 4 55/100, GLM-5.3 43/100), so choose by what matters most for your work: Solar Pro 4 on price and GLM-5.3 for long inputs. The score weighs price 50%, inputs & features 30%, context window 20%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
- CapabilityNot enough dataNo independent benchmark covers every model here yet
- Lowest priceSolar Pro 4 and Muse Glimmer 30BSolar Pro 4 $0.525 · Muse Glimmer 30B $0.525 · GLM-5.3 $2.15 per 1M tokens (3:1 blend)
- Longest contextGLM-5.3GLM-5.3 1,000,000 · Solar Pro 4 524,288 · Muse Glimmer 30B 131,072 tokens
- Widest inputsMuse Glimmer 30BSolar Pro 4: Text · GLM-5.3: Text · Muse Glimmer 30B: Text, Images
- Self-hostingGLM-5.3 and Muse Glimmer 30BPublishes downloadable weights (Apache 2.0)
| Measure | Weight | Solar Pro 4 | GLM-5.3 | Muse Glimmer 30B |
|---|---|---|---|---|
| Price | 50% | 63 | 34 | 63 |
| Inputs & features | 30% | 45 | 45 | 70 |
| Context window | 20% | 49 | 60 | 24 |
| Overall | 100% | 55/100 | 43/100 | 57/100 |
Left out because at least one model lacks the data: capability. The remaining weights were rescaled.
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Solar Pro 4Upstage | GLM-5.3Z.ai (Zhipu) | Muse Glimmer 30BMeta |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | 155.8 | — |
| ECI rank | — | #24 of 148 | — |
| GPQA DiamondGraduate-level science questions | — | 90.9% | — |
| FrontierMath Tiers 1–3Research-level mathematics | — | 68.8% | — |
| OTIS Mock AIME 2024–2025Competition mathematics | — | 91.1% | — |
| SimpleQA VerifiedShort factual questions | — | 41.0% | — |
| Price per million tokens | |||
| Input | $0.30 (best) | $1.40 | $0.30 (best) |
| Output | $1.20 (best) | $4.40 | $1.20 (best) |
| Cached input | $0.06 (best) | $0.26 | — |
| Blended (3:1) | $0.525 (best) | $2.15 | $0.525 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Upstage API | Official Z.AI API | Median of 10 providers |
| Limits | |||
| Context window | 524,288 tokens | 1,000,000 tokens (best) | 131,072 tokens |
| Max output | 131,072 tokens | 131,072 tokens | 131,072 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | No | Yes |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | Yesminimal · low · medium · high · xhigh · max | Yeslow · high · max | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | Yes | Yes |
| Availability | |||
| Weights | Proprietary | Open | OpenApache 2.0 |
| API model ID | solar-pro4 | glm-5.3 | — |
| API providers | 3 | 62 (best) | 12 |
| Released | Aug 6, 2026 | Aug 14, 2026 | Aug 10, 2026 |
| Knowledge cutoff | Feb 2026 | — | Jan 4, 2026 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Solar Pro 4$5.40
- GLM-5.3$22.80
- Muse Glimmer 30B$5.40
Which should you choose?
Which is better: Solar Pro 4, GLM-5.3 or Muse Glimmer 30B?
It is close. Our weighted score puts them within 3 points (Muse Glimmer 30B 57/100, Solar Pro 4 55/100, GLM-5.3 43/100), so choose by what matters most for your work: Solar Pro 4 on price and GLM-5.3 for long inputs. The score weighs price 50%, inputs & features 30%, context window 20%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
Which is cheaper, Solar Pro 4, GLM-5.3 or Muse Glimmer 30B?
Solar Pro 4 is cheaper at $0.30 input / $1.20 output per million tokens (official Upstage API price). Muse Glimmer 30B costs $0.30 input / $1.20 output per million tokens (median across 10 API providers); 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 $0.525 per million tokens for Solar Pro 4 versus $0.525 for Muse Glimmer 30B (1× as much) and $2.15 for GLM-5.3 (4.1× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Solar Pro 4 has not been scored yet, GLM-5.3 has an ECI of 155.8 and Muse Glimmer 30B has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Solar Pro 4, GLM-5.3 and Muse Glimmer 30B yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. All three support tool calling for agent workflows.
Which has the bigger context window?
GLM-5.3 has the largest context window at 1,000,000 tokens, against 524,288 for Solar Pro 4 and 131,072 for Muse Glimmer 30B. Maximum output per response: Solar Pro 4 up to 131,072, GLM-5.3 up to 131,072, Muse Glimmer 30B up to 131,072 tokens.
Which can read images, PDFs, audio or video?
Solar Pro 4 accepts text; GLM-5.3 accepts text; Muse Glimmer 30B accepts text and images. Muse Glimmer 30B handles the widest range of inputs.
Are any of these open source?
GLM-5.3 and Muse Glimmer 30B publishes its weights (Apache 2.0) and can be self-hosted; Solar Pro 4 is proprietary.
Which is newer?
GLM-5.3 is the newest, released Aug 14, 2026. Muse Glimmer 30B came out Aug 10, 2026; Solar Pro 4 came out Aug 6, 2026. Knowledge cutoff: Solar Pro 4 Feb 2026, Muse Glimmer 30B Jan 4, 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.
