Macaron V1 Tall vs Perceptron Mk1 vs Solar Pro 4
Too close to call on our weighted score (Macaron V1 Tall 55, Solar Pro 4 53, Perceptron Mk1 45). The right pick depends on what you value most.
Mindlab
Macaron V1 Tall
55/100- ECI—
- Price$0.247 / $1.43
- Context262K
Perceptron
Perceptron Mk1
45/100- ECI—
- Price$0.15 / $1.50
- Context33K
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, Perceptron Mk1 45/100), so choose by what matters most for your work: Perceptron Mk1 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 pricePerceptron Mk1Perceptron Mk1 $0.487 · 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 · Perceptron Mk1 32,768 tokens
- Widest inputsPerceptron Mk1Macaron V1 Tall: Text, Images · Perceptron Mk1: Text, Images, Video · Solar Pro 4: Text
- Self-hostingMacaron V1 TallPublishes downloadable weights (MIT)
| Measure | Weight | Macaron V1 Tall | Perceptron Mk1 | Solar Pro 4 |
|---|---|---|---|---|
| Price | 50% | 60 | 62 | 60 |
| Inputs & features | 30% | 60 | 45 | 45 |
| Context window | 20% | 37 | 0 | 49 |
| Overall | 100% | 55/100 | 45/100 | 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 | Perceptron Mk1Perceptron | Solar Pro 4Upstage |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.247 | $0.15 (best) | $0.30 |
| Output | $1.43 | $1.50 | $1.20 (best) |
| Cached input | — | — | $0.06 |
| Blended (3:1) | $0.543 | $0.487 (best) | $0.525 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 1 providers | Median of 3 providers | Official Upstage API |
| Limits | |||
| Context window | 262,144 tokens | 32,768 tokens | 524,288 tokens (best) |
| Max output | 32,768 tokens | 8,192 tokens | 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 | Yes | No |
| Reasoning | Yes | Yes | Yesminimal · low · medium · high · xhigh · max |
| Tool calling | Yes | No | Yes |
| Structured output | No | No | Yes |
| Availability | |||
| Weights | OpenMIT | Proprietary | Proprietary |
| API model ID | — | — | solar-pro4 |
| API providers | 1 | 3 | 6 (best) |
| Released | Jul 21, 2026 | May 12, 2026 | Aug 6, 2026 |
| Knowledge cutoff | — | — | 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
- Perceptron Mk1$4.50
- Solar Pro 4$5.40
Which should you choose?
Which is better: Macaron V1 Tall, Perceptron Mk1 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, Perceptron Mk1 45/100), so choose by what matters most for your work: Perceptron Mk1 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, Perceptron Mk1 or Solar Pro 4?
Perceptron Mk1 is cheaper at $0.15 input / $1.50 output per million tokens (median across 3 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 $0.487 per million tokens for Perceptron Mk1 versus $0.525 for Solar Pro 4 (1.1× as much) and $0.543 for Macaron V1 Tall (1.1× 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, Perceptron Mk1 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, Perceptron Mk1 and Solar Pro 4 yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Note that Perceptron Mk1 does not support tool calling, which most coding agents need.
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 32,768 for Perceptron Mk1. Maximum output per response: Macaron V1 Tall up to 32,768, Perceptron Mk1 up to 8,192, Solar Pro 4 up to 131,072 tokens.
Which can read images, PDFs, audio or video?
Macaron V1 Tall accepts text and images; Perceptron Mk1 accepts text, images and video; Solar Pro 4 accepts text. Perceptron Mk1 handles the widest range of inputs.
Are any of these open source?
Macaron V1 Tall publishes its weights (MIT) and can be self-hosted; Perceptron Mk1 and Solar Pro 4 is proprietary.
Which is newer?
Solar Pro 4 is the newest, released Aug 6, 2026. Macaron V1 Tall came out Jul 21, 2026; Perceptron Mk1 came out May 12, 2026. Knowledge cutoff: 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.
