Macaron V1 Tall vs Solar Pro 4 vs Muse Glimmer 30B
Too close to call on our weighted score (Macaron V1 Tall 55, Solar Pro 4 53, Muse Glimmer 30B 53). The right pick depends on what you value most.
Mindlab
Macaron V1 Tall
55/100- ECI—
- Price$0.247 / $1.43
- Context262K
UpstageSolar Pro 4
53/100- ECI—
- Price$0.30 / $1.20
- Context524K
MetaMuse Glimmer 30B
53/100- ECI—
- Price$0.30 / $1.20
- Context131K
Too close to call
It is close. Our weighted score puts them within 2 points (Macaron V1 Tall 55/100, Solar Pro 4 53/100, Muse Glimmer 30B 53/100), so choose by what matters most for your work: Solar Pro 4 on price and Solar Pro 4 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 · Macaron V1 Tall $0.543 per 1M tokens (3:1 blend)
- Longest contextSolar Pro 4Solar Pro 4 524,288 · Macaron V1 Tall 262,144 · Muse Glimmer 30B 131,072 tokens
- Widest inputsMacaron V1 Tall and Muse Glimmer 30BMacaron V1 Tall: Text, Images · Solar Pro 4: Text · Muse Glimmer 30B: Text, Images
- Self-hostingMacaron V1 Tall and Muse Glimmer 30BPublishes downloadable weights (MIT and Apache 2.0)
| Measure | Weight | Macaron V1 Tall | Solar Pro 4 | Muse Glimmer 30B |
|---|---|---|---|---|
| Price | 50% | 60 | 60 | 60 |
| Inputs & features | 30% | 60 | 45 | 60 |
| Context window | 20% | 37 | 49 | 25 |
| Overall | 100% | 55/100 | 53/100 | 53/100 |
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 | Macaron V1 TallMindlab | Solar Pro 4Upstage | Muse Glimmer 30BMeta |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.247 (best) | $0.30 | $0.30 |
| Output | $1.43 | $1.20 (best) | $1.20 (best) |
| Cached input | — | $0.06 | — |
| Blended (3:1) | $0.543 | $0.525 (best) | $0.525 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 1 providers | Official Upstage API | Median of 11 providers |
| Limits | |||
| Context window | 262,144 tokens | 524,288 tokens (best) | 131,072 tokens |
| Max output | 32,768 tokens | 131,072 tokens (best) | 131,072 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | No | Yes |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | Yes | Yesminimal · low · medium · high · xhigh · max | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | Yes | No |
| Availability | |||
| Weights | OpenMIT | Proprietary | OpenApache 2.0 |
| API model ID | — | solar-pro4 | — |
| API providers | 1 | 6 | 13 (best) |
| Released | Jul 21, 2026 | Aug 6, 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.
- Macaron V1 Tall$5.33
- Solar Pro 4$5.40
- Muse Glimmer 30B$5.40
Which should you choose?
Which is better: Macaron V1 Tall, Solar Pro 4 or Muse Glimmer 30B?
It is close. Our weighted score puts them within 2 points (Macaron V1 Tall 55/100, Solar Pro 4 53/100, Muse Glimmer 30B 53/100), so choose by what matters most for your work: Solar Pro 4 on price and Solar Pro 4 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, Macaron V1 Tall, Solar Pro 4 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 11 API providers); Macaron V1 Tall costs $0.247 input / $1.43 output per million tokens (median across 1 API provider). 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 $0.543 for Macaron V1 Tall (1× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Macaron V1 Tall has not been scored yet, Solar Pro 4 has not been scored yet and Muse Glimmer 30B has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Macaron V1 Tall, Solar Pro 4 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?
Solar Pro 4 has the largest context window at 524,288 tokens, against 262,144 for Macaron V1 Tall and 131,072 for Muse Glimmer 30B. Maximum output per response: Macaron V1 Tall up to 32,768, Solar Pro 4 up to 131,072, Muse Glimmer 30B up to 131,072 tokens.
Which can read images, PDFs, audio or video?
Macaron V1 Tall accepts text and images; Solar Pro 4 accepts text; Muse Glimmer 30B accepts text and images. Macaron V1 Tall handles the widest range of inputs.
Are any of these open source?
Macaron V1 Tall and Muse Glimmer 30B publishes its weights (MIT and Apache 2.0) and can be self-hosted; Solar Pro 4 is proprietary.
Which is newer?
Muse Glimmer 30B is the newest, released Aug 10, 2026. Solar Pro 4 came out Aug 6, 2026; Macaron V1 Tall came out Jul 21, 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 Nov 2, 2026.
