Claude Mythos 5.1 vs Macaron V1 Venti vs GPT-6 Astra (Fast)
Too close to call on our weighted score (GPT-6 Astra (Fast) 66, Claude Mythos 5.1 66, Macaron V1 Venti 45). The right pick depends on what you value most.
AnthropicClaude Mythos 5.1
66/100- ECI—
- Price—
- Context1M
Mindlab
Macaron V1 Venti
45/100- ECI—
- Price$0.825 / $2.48
- Context1.05M
OpenAIGPT-6 Astra (Fast)
66/100- ECI—
- Price$20.00 / $100.00
- Context1.05M
Too close to call
It is close. Our weighted score puts them within a point (GPT-6 Astra (Fast) 66/100, Claude Mythos 5.1 66/100, Macaron V1 Venti 45/100), so choose by what matters most for your work: Macaron V1 Venti on price. The score weighs inputs & features 60%, context window 40%. 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 priceMacaron V1 VentiMacaron V1 Venti $1.24 · GPT-6 Astra (Fast) $40.00 per 1M tokens (3:1 blend) · Claude Mythos 5.1 unpriced
- Longest contextGPT-6 Astra (Fast) and Macaron V1 VentiGPT-6 Astra (Fast) 1,050,000 · Macaron V1 Venti 1,048,576 · Claude Mythos 5.1 1,000,000 tokens
- Widest inputsClaude Mythos 5.1 and GPT-6 Astra (Fast)Claude Mythos 5.1: Text, Images, PDFs · Macaron V1 Venti: Text · GPT-6 Astra (Fast): Text, Images, PDFs
- Self-hostingMacaron V1 VentiPublishes downloadable weights (MIT)
| Measure | Weight | Claude Mythos 5.1 | Macaron V1 Venti | GPT-6 Astra (Fast) |
|---|---|---|---|---|
| Inputs & features | 60% | 70 | 35 | 70 |
| Context window | 40% | 60 | 61 | 61 |
| Overall | 100% | 66/100 | 45/100 | 66/100 |
Left out because at least one model lacks the data: capability and price. The remaining weights were rescaled.
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Claude Mythos 5.1Anthropic | Macaron V1 VentiMindlab | GPT-6 Astra (Fast)OpenAI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | — | $0.825 (best) | $20.00 |
| Output | — | $2.48 (best) | $100.00 |
| Cached input | — | — | — |
| Blended (3:1) | — | $1.24 (best) | $40.00 |
| Long-context rate | — | Same rate | Same rate |
| Price source | — | Median of 1 providers | Median of 1 providers |
| Limits | |||
| Context window | 1,000,000 tokens | 1,048,576 tokens | 1,050,000 tokens (best) |
| Max output | 128,000 tokens | 131,072 tokens (best) | 128,000 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | No | Yes |
| PDFs | Yes | No | Yes |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | Yes | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Proprietary | OpenMIT | Proprietary |
| API model ID | — | — | — |
| API providers | — | 1 | 1 |
| Released | Sep 1, 2026 | Jul 21, 2026 | Sep 4, 2026 |
| Knowledge cutoff | Jun 2026 | — | — |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Claude Mythos 5.1—
- Macaron V1 Venti$13.20
- GPT-6 Astra (Fast)$400.00
Which should you choose?
Which is better: Claude Mythos 5.1, Macaron V1 Venti or GPT-6 Astra (Fast)?
It is close. Our weighted score puts them within a point (GPT-6 Astra (Fast) 66/100, Claude Mythos 5.1 66/100, Macaron V1 Venti 45/100), so choose by what matters most for your work: Macaron V1 Venti on price. The score weighs inputs & features 60%, context window 40%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
Which is cheaper, Claude Mythos 5.1, Macaron V1 Venti or GPT-6 Astra (Fast)?
Macaron V1 Venti is cheaper at $0.825 input / $2.48 output per million tokens (median across 1 API provider). GPT-6 Astra (Fast) costs $20.00 input / $100.00 output per million tokens (median across 1 API provider). At a typical mix of three input tokens to one output token, that is $1.24 per million tokens for Macaron V1 Venti versus $40.00 for GPT-6 Astra (Fast) (32× as much). Claude Mythos 5.1 has no published per-token price.
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Claude Mythos 5.1 has not been scored yet, Macaron V1 Venti has not been scored yet and GPT-6 Astra (Fast) has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Claude Mythos 5.1, Macaron V1 Venti and GPT-6 Astra (Fast) 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?
GPT-6 Astra (Fast) and Macaron V1 Venti have the largest context windows (1,050,000 and 1,048,576 tokens), against 1,000,000 for Claude Mythos 5.1. Maximum output per response: Claude Mythos 5.1 up to 128,000, Macaron V1 Venti up to 131,072, GPT-6 Astra (Fast) up to 128,000 tokens.
Which can read images, PDFs, audio or video?
Claude Mythos 5.1 accepts text, images and PDFs; Macaron V1 Venti accepts text; GPT-6 Astra (Fast) accepts text, images and PDFs. Claude Mythos 5.1 handles the widest range of inputs.
Are any of these open source?
Macaron V1 Venti publishes its weights (MIT) and can be self-hosted; Claude Mythos 5.1 and GPT-6 Astra (Fast) is proprietary.
Which is newer?
GPT-6 Astra (Fast) is the newest, released Sep 4, 2026. Claude Mythos 5.1 came out Sep 1, 2026; Macaron V1 Venti came out Jul 21, 2026. Knowledge cutoff: Claude Mythos 5.1 Jun 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.
