Ling 3.0 Flash Fin vs GPT-6 Astra (Fast) vs Laguna S 2.1
Laguna S 2.1 comes out ahead, 68 to 63 and 33 on our weighted score.
inclusionAILing 3.0 Flash Fin
63/100- ECI—
- Price$0.075 / $0.22
- Context262K
OpenAIGPT-6 Astra (Fast)
33/100- ECI—
- Price$20.00 / $100.00
- Context1.05M
- Our pick
PoolsideLaguna S 2.1
68/100- ECI—
- Price$0.09 / $0.18
- Context1.05M
Laguna S 2.1 is our pick
Laguna S 2.1 is the better all-round choice, scoring 68/100 against Ling 3.0 Flash Fin (63) and GPT-6 Astra (Fast) (33). GPT-6 Astra (Fast) wins on inputs & features. 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 priceLing 3.0 Flash FinLing 3.0 Flash Fin $0.111 · Laguna S 2.1 $0.113 · GPT-6 Astra (Fast) $40.00 per 1M tokens (3:1 blend)
- Longest contextGPT-6 Astra (Fast) and Laguna S 2.1GPT-6 Astra (Fast) 1,050,000 · Laguna S 2.1 1,048,576 · Ling 3.0 Flash Fin 262,144 tokens
- Widest inputsGPT-6 Astra (Fast)Ling 3.0 Flash Fin: Text · GPT-6 Astra (Fast): Text, Images, PDFs · Laguna S 2.1: Text
- Self-hostingLing 3.0 Flash Fin and Laguna S 2.1Publishes downloadable weights (MIT)
| Measure | Weight | Ling 3.0 Flash Fin | GPT-6 Astra (Fast) | Laguna S 2.1 |
|---|---|---|---|---|
| Price | 50% | 91 | 0 | 91 |
| Inputs & features | 30% | 35 | 70 | 35 |
| Context window | 20% | 37 | 61 | 61 |
| Overall | 100% | 63/100 | 33/100 | 68/100 |
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 | Ling 3.0 Flash FininclusionAI | GPT-6 Astra (Fast)OpenAI | Laguna S 2.1Poolside |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.075 (best) | $20.00 | $0.09 |
| Output | $0.22 | $100.00 | $0.18 (best) |
| Cached input | — | — | — |
| Blended (3:1) | $0.111 (best) | $40.00 | $0.113 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 2 providers | Median of 1 providers | Median of 2 providers |
| Limits | |||
| Context window | 262,144 tokens | 1,050,000 tokens (best) | 1,048,576 tokens |
| Max output | 32,768 tokens | 128,000 tokens (best) | 32,768 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | No |
| PDFs | No | Yes | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | Yes | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | OpenMIT | Proprietary | Open |
| API model ID | — | — | poolside/laguna-s-2.1 |
| API providers | 3 | 1 | 4 (best) |
| Released | Aug 27, 2026 | Sep 4, 2026 | Jul 21, 2026 |
| Knowledge cutoff | — | — | — |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Ling 3.0 Flash Fin$1.19
- GPT-6 Astra (Fast)$400.00
- Laguna S 2.1$1.26
Which should you choose?
Which is better: Ling 3.0 Flash Fin, GPT-6 Astra (Fast) or Laguna S 2.1?
Laguna S 2.1 is the better all-round choice, scoring 68/100 against Ling 3.0 Flash Fin (63) and GPT-6 Astra (Fast) (33). GPT-6 Astra (Fast) wins on inputs & features. 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, Ling 3.0 Flash Fin, GPT-6 Astra (Fast) or Laguna S 2.1?
Ling 3.0 Flash Fin is cheaper at $0.075 input / $0.22 output per million tokens (median across 2 API providers). Laguna S 2.1 costs $0.09 input / $0.18 output per million tokens (median across 2 API providers; free on Poolside); 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 $0.113 for Laguna S 2.1 (1× as much) and $40.00 for GPT-6 Astra (Fast) (360× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Ling 3.0 Flash Fin has not been scored yet, GPT-6 Astra (Fast) has not been scored yet and Laguna S 2.1 has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Ling 3.0 Flash Fin, GPT-6 Astra (Fast) and Laguna S 2.1 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) and Laguna S 2.1 have the largest context windows (1,050,000 and 1,048,576 tokens), against 262,144 for Ling 3.0 Flash Fin. Maximum output per response: Ling 3.0 Flash Fin up to 32,768, GPT-6 Astra (Fast) up to 128,000, Laguna S 2.1 up to 32,768 tokens.
Which can read images, PDFs, audio or video?
Ling 3.0 Flash Fin accepts text; GPT-6 Astra (Fast) accepts text, images and PDFs; Laguna S 2.1 accepts text. GPT-6 Astra (Fast) handles the widest range of inputs.
Are any of these open source?
Ling 3.0 Flash Fin and Laguna S 2.1 publishes its weights (MIT) and can be self-hosted; GPT-6 Astra (Fast) is proprietary.
Which is newer?
GPT-6 Astra (Fast) is the newest, released Sep 4, 2026. Ling 3.0 Flash Fin came out Aug 27, 2026; Laguna S 2.1 came out Jul 21, 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.
