GPT-6 Astra (Fast) vs Ling 3.0 Flash Fin vs Claude Mythos 5.1
Too close to call on our weighted score (GPT-6 Astra (Fast) 66, Claude Mythos 5.1 66, Ling 3.0 Flash Fin 36). The right pick depends on what you value most.
OpenAIGPT-6 Astra (Fast)
66/100- ECI—
- Price$20.00 / $100.00
- Context1.05M
inclusionAILing 3.0 Flash Fin
36/100- ECI—
- Price$0.075 / $0.22
- Context262K
AnthropicClaude Mythos 5.1
66/100- ECI—
- Price—
- Context1M
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, Ling 3.0 Flash Fin 36/100), so choose by what matters most for your work: Ling 3.0 Flash Fin on price and GPT-6 Astra (Fast) for long inputs. 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 priceLing 3.0 Flash FinLing 3.0 Flash Fin $0.111 · GPT-6 Astra (Fast) $40.00 per 1M tokens (3:1 blend) · Claude Mythos 5.1 unpriced
- Longest contextGPT-6 Astra (Fast)GPT-6 Astra (Fast) 1,050,000 · Claude Mythos 5.1 1,000,000 · Ling 3.0 Flash Fin 262,144 tokens
- Widest inputsGPT-6 Astra (Fast) and Claude Mythos 5.1GPT-6 Astra (Fast): Text, Images, PDFs · Ling 3.0 Flash Fin: Text · Claude Mythos 5.1: Text, Images, PDFs
- Self-hostingLing 3.0 Flash FinPublishes downloadable weights (MIT)
| Measure | Weight | GPT-6 Astra (Fast) | Ling 3.0 Flash Fin | Claude Mythos 5.1 |
|---|---|---|---|---|
| Inputs & features | 60% | 70 | 35 | 70 |
| Context window | 40% | 61 | 37 | 60 |
| Overall | 100% | 66/100 | 36/100 | 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 | GPT-6 Astra (Fast)OpenAI | Ling 3.0 Flash FininclusionAI | Claude Mythos 5.1Anthropic |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $20.00 | $0.075 (best) | — |
| Output | $100.00 | $0.22 (best) | — |
| Cached input | — | — | — |
| Blended (3:1) | $40.00 | $0.111 (best) | — |
| Long-context rate | Same rate | Same rate | — |
| Price source | Median of 1 providers | Median of 2 providers | — |
| Limits | |||
| Context window | 1,050,000 tokens (best) | 262,144 tokens | 1,000,000 tokens |
| Max output | 128,000 tokens (best) | 32,768 tokens | 128,000 tokens (best) |
| 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 | 3 (best) | — |
| Released | Sep 4, 2026 | Aug 27, 2026 | Sep 1, 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.
- GPT-6 Astra (Fast)$400.00
- Ling 3.0 Flash Fin$1.19
- Claude Mythos 5.1—
Which should you choose?
Which is better: GPT-6 Astra (Fast), Ling 3.0 Flash Fin or Claude Mythos 5.1?
It is close. Our weighted score puts them within a point (GPT-6 Astra (Fast) 66/100, Claude Mythos 5.1 66/100, Ling 3.0 Flash Fin 36/100), so choose by what matters most for your work: Ling 3.0 Flash Fin on price and GPT-6 Astra (Fast) for long inputs. 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, GPT-6 Astra (Fast), Ling 3.0 Flash Fin or Claude Mythos 5.1?
Ling 3.0 Flash Fin is cheaper at $0.075 input / $0.22 output per million tokens (median across 2 API providers). 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 $0.111 per million tokens for Ling 3.0 Flash Fin versus $40.00 for GPT-6 Astra (Fast) (360× 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. GPT-6 Astra (Fast) has not been scored yet, Ling 3.0 Flash Fin has not been scored yet and Claude Mythos 5.1 has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for GPT-6 Astra (Fast), Ling 3.0 Flash Fin and Claude Mythos 5.1 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) has the largest context window at 1,050,000 tokens, against 1,000,000 for Claude Mythos 5.1 and 262,144 for Ling 3.0 Flash Fin. Maximum output per response: GPT-6 Astra (Fast) up to 128,000, Ling 3.0 Flash Fin up to 32,768, Claude Mythos 5.1 up to 128,000 tokens.
Which can read images, PDFs, audio or video?
GPT-6 Astra (Fast) accepts text, images and PDFs; Ling 3.0 Flash Fin accepts text; Claude Mythos 5.1 accepts text, images and PDFs. GPT-6 Astra (Fast) handles the widest range of inputs.
Are any of these open source?
Ling 3.0 Flash Fin publishes its weights (MIT) and can be self-hosted; GPT-6 Astra (Fast) and Claude Mythos 5.1 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; Ling 3.0 Flash Fin came out Aug 27, 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.
