Macaron V1 Tall vs Muse Glimmer 30B vs Solar Pro 4
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
MetaMuse Glimmer 30B
53/100- ECI—
- Price$0.30 / $1.20
- Context131K
UpstageSolar Pro 4
53/100- ECI—
- Price$0.30 / $1.20
- Context524K
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: Muse Glimmer 30B 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 priceMuse Glimmer 30B and Solar Pro 4Muse Glimmer 30B $0.525 · Solar Pro 4 $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 · Muse Glimmer 30B: Text, Images · Solar Pro 4: Text
- Self-hostingMacaron V1 Tall and Muse Glimmer 30BPublishes downloadable weights (MIT and Apache 2.0)
| Measure | Weight | Macaron V1 Tall | Muse Glimmer 30B | Solar Pro 4 |
|---|---|---|---|---|
| Price | 50% | 60 | 60 | 60 |
| Inputs & features | 30% | 60 | 60 | 45 |
| Context window | 20% | 37 | 25 | 49 |
| 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 | Muse Glimmer 30BMeta | Solar Pro 4Upstage |
|---|---|---|---|
| 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 | Median of 11 providers | Official Upstage API |
| Limits | |||
| Context window | 262,144 tokens | 131,072 tokens | 524,288 tokens (best) |
| Max output | 32,768 tokens | 131,072 tokens (best) | 131,072 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | Yes | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | Yes | Yes | Yesminimal · low · medium · high · xhigh · max |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | Yes |
| Availability | |||
| Weights | OpenMIT | OpenApache 2.0 | Proprietary |
| API model ID | — | — | solar-pro4 |
| API providers | 1 | 13 (best) | 6 |
| Released | Jul 21, 2026 | Aug 10, 2026 | Aug 6, 2026 |
| Knowledge cutoff | — | Jan 4, 2026 | Feb 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
- Muse Glimmer 30B$5.40
- Solar Pro 4$5.40
Which should you choose?
Which is better: Macaron V1 Tall, Muse Glimmer 30B or Solar Pro 4?
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: Muse Glimmer 30B 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, Muse Glimmer 30B or Solar Pro 4?
Muse Glimmer 30B is cheaper at $0.30 input / $1.20 output per million tokens (median across 11 API providers). Solar Pro 4 costs $0.30 input / $1.20 output per million tokens (official Upstage API price); 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 Muse Glimmer 30B versus $0.525 for Solar Pro 4 (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, Muse Glimmer 30B has not been scored yet and Solar Pro 4 has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Macaron V1 Tall, Muse Glimmer 30B and Solar Pro 4 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, Muse Glimmer 30B up to 131,072, Solar Pro 4 up to 131,072 tokens.
Which can read images, PDFs, audio or video?
Macaron V1 Tall accepts text and images; Muse Glimmer 30B accepts text and images; Solar Pro 4 accepts text. 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: Muse Glimmer 30B Jan 4, 2026, Solar Pro 4 Feb 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.
