Toast 1 vs Laguna XS 2.1 vs DeepSeek V4 Flash Vision Exp
Too close to call on our weighted score (DeepSeek V4 Flash Vision Exp 70, Laguna XS 2.1 68, Toast 1 47). The right pick depends on what you value most.
Mixedbread
Toast 1
47/100- ECI—
- Price$0.30 / $0.72
- Context131K
PoolsideLaguna XS 2.1
68/100- ECI—
- Price$0.06 / $0.12
- Context262K
DeepSeekDeepSeek V4 Flash Vision Exp
70/100- ECI—
- Price$0.216 / $0.647
- Context1M
Too close to call
It is close. Our weighted score puts them within 2 points (DeepSeek V4 Flash Vision Exp 70/100, Laguna XS 2.1 68/100, Toast 1 47/100), so choose by what matters most for your work: Laguna XS 2.1 on price and DeepSeek V4 Flash Vision Exp 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 priceLaguna XS 2.1Laguna XS 2.1 $0.075 · DeepSeek V4 Flash Vision Exp $0.323 · Toast 1 $0.405 per 1M tokens (3:1 blend)
- Longest contextDeepSeek V4 Flash Vision ExpDeepSeek V4 Flash Vision Exp 1,000,000 · Laguna XS 2.1 262,144 · Toast 1 131,000 tokens
- Widest inputsDeepSeek V4 Flash Vision ExpToast 1: Text · Laguna XS 2.1: Text · DeepSeek V4 Flash Vision Exp: Text, Images
- Self-hostingLaguna XS 2.1 and DeepSeek V4 Flash Vision ExpPublishes downloadable weights (MIT)
| Measure | Weight | Toast 1 | Laguna XS 2.1 | DeepSeek V4 Flash Vision Exp |
|---|---|---|---|---|
| Price | 50% | 68 | 100 | 73 |
| Inputs & features | 30% | 25 | 35 | 70 |
| Context window | 20% | 24 | 37 | 60 |
| Overall | 100% | 47/100 | 68/100 | 70/100 |
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 | Toast 1Mixedbread | Laguna XS 2.1Poolside | DeepSeek V4 Flash Vision ExpDeepSeek |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.30 | $0.06 (best) | $0.216 |
| Output | $0.72 | $0.12 (best) | $0.647 |
| Cached input | — | — | — |
| Blended (3:1) | $0.405 | $0.075 (best) | $0.323 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 1 providers | Median of 1 providers | Median of 15 providers |
| Limits | |||
| Context window | 131,000 tokens | 262,144 tokens | 1,000,000 tokens (best) |
| Max output | 4,000 tokens | 32,768 tokens | 384,000 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | No | Yes |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | No | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | Yes |
| Availability | |||
| Weights | Proprietary | Open | OpenMIT |
| API model ID | — | poolside/laguna-xs-2.1 | — |
| API providers | 1 | 4 | 15 (best) |
| Released | Aug 13, 2026 | Jul 2, 2026 | Aug 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.
- Toast 1$4.44
- Laguna XS 2.1$0.84
- DeepSeek V4 Flash Vision Exp$3.45
Which should you choose?
Which is better: Toast 1, Laguna XS 2.1 or DeepSeek V4 Flash Vision Exp?
It is close. Our weighted score puts them within 2 points (DeepSeek V4 Flash Vision Exp 70/100, Laguna XS 2.1 68/100, Toast 1 47/100), so choose by what matters most for your work: Laguna XS 2.1 on price and DeepSeek V4 Flash Vision Exp 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, Toast 1, Laguna XS 2.1 or DeepSeek V4 Flash Vision Exp?
Laguna XS 2.1 is cheaper at $0.06 input / $0.12 output per million tokens (median across 1 API provider; free on Poolside). DeepSeek V4 Flash Vision Exp costs $0.216 input / $0.647 output per million tokens (median across 15 API providers); Toast 1 costs $0.30 input / $0.72 output per million tokens (median across 1 API provider). At a typical mix of three input tokens to one output token, that is $0.075 per million tokens for Laguna XS 2.1 versus $0.323 for DeepSeek V4 Flash Vision Exp (4.3× as much) and $0.405 for Toast 1 (5.4× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Toast 1 has not been scored yet, Laguna XS 2.1 has not been scored yet and DeepSeek V4 Flash Vision Exp has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Toast 1, Laguna XS 2.1 and DeepSeek V4 Flash Vision Exp 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?
DeepSeek V4 Flash Vision Exp has the largest context window at 1,000,000 tokens, against 262,144 for Laguna XS 2.1 and 131,000 for Toast 1. Maximum output per response: Toast 1 up to 4,000, Laguna XS 2.1 up to 32,768, DeepSeek V4 Flash Vision Exp up to 384,000 tokens.
Which can read images, PDFs, audio or video?
Toast 1 accepts text; Laguna XS 2.1 accepts text; DeepSeek V4 Flash Vision Exp accepts text and images. DeepSeek V4 Flash Vision Exp handles the widest range of inputs.
Are any of these open source?
Laguna XS 2.1 and DeepSeek V4 Flash Vision Exp publishes its weights (MIT) and can be self-hosted; Toast 1 is proprietary.
Which is newer?
DeepSeek V4 Flash Vision Exp is the newest, released Aug 21, 2026. Toast 1 came out Aug 13, 2026; Laguna XS 2.1 came out Jul 2, 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 Oct 4, 2026.
