GPT-5.6 Cyber vs Laguna XS 2.1 vs Claude Mythos 5
Laguna XS 2.1 comes out ahead, 68 to 36 and 30 on our weighted score, and it is the cheaper option too.
OpenAIGPT-5.6 Cyber
30/100- ECI—
- Price$12.50 / $75.00
- Context400K
- Our pick
PoolsideLaguna XS 2.1
68/100- ECI—
- Price$0.06 / $0.12
- Context262K
AnthropicClaude Mythos 5
36/100- ECI—
- Price$10.00 / $50.00
- Context1M
Laguna XS 2.1 is our pick
Laguna XS 2.1 is the better all-round choice, scoring 68/100 against Claude Mythos 5 (36) and GPT-5.6 Cyber (30). It leads on price. Claude Mythos 5 wins on inputs & features and context window. 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 · Claude Mythos 5 $20.00 · GPT-5.6 Cyber $28.13 per 1M tokens (3:1 blend)
- Longest contextClaude Mythos 5Claude Mythos 5 1,000,000 · GPT-5.6 Cyber 400,000 · Laguna XS 2.1 262,144 tokens
- Widest inputsClaude Mythos 5GPT-5.6 Cyber: Text, Images · Laguna XS 2.1: Text · Claude Mythos 5: Text, Images, PDFs
- Self-hostingLaguna XS 2.1Publishes downloadable weights
| Measure | Weight | GPT-5.6 Cyber | Laguna XS 2.1 | Claude Mythos 5 |
|---|---|---|---|---|
| Price | 50% | 0 | 100 | 0 |
| Inputs & features | 30% | 70 | 35 | 80 |
| Context window | 20% | 44 | 37 | 60 |
| Overall | 100% | 30/100 | 68/100 | 36/100 |
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 | GPT-5.6 CyberOpenAI | Laguna XS 2.1Poolside | Claude Mythos 5Anthropic |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $12.50 | $0.06 (best) | $10.00 |
| Output | $75.00 | $0.12 (best) | $50.00 |
| Cached input | $1.25 | — | — |
| Blended (3:1) | $28.13 | $0.075 (best) | $20.00 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official OpenAI API | Median of 1 providers | Median of 2 providers |
| Limits | |||
| Context window | 400,000 tokens | 262,144 tokens | 1,000,000 tokens (best) |
| 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 | No | No | Yes |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | Yeslow · medium · high · xhigh · max | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | No | Yes |
| Availability | |||
| Weights | Proprietary | Open | Proprietary |
| API model ID | gpt-daybreak-red-latest | poolside/laguna-xs-2.1 | — |
| API providers | 1 | 4 (best) | 2 |
| Released | Aug 7, 2026 | Jul 2, 2026 | Jun 9, 2026 |
| Knowledge cutoff | Feb 16, 2026 | — | Jan 31, 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-5.6 Cyber$275.00
- Laguna XS 2.1$0.84
- Claude Mythos 5$200.00
Which should you choose?
Which is better: GPT-5.6 Cyber, Laguna XS 2.1 or Claude Mythos 5?
Laguna XS 2.1 is the better all-round choice, scoring 68/100 against Claude Mythos 5 (36) and GPT-5.6 Cyber (30). It leads on price. Claude Mythos 5 wins on inputs & features and context window. 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, GPT-5.6 Cyber, Laguna XS 2.1 or Claude Mythos 5?
Laguna XS 2.1 is cheaper at $0.06 input / $0.12 output per million tokens (median across 1 API provider; free on Poolside). Claude Mythos 5 costs $10.00 input / $50.00 output per million tokens (median across 2 API providers); GPT-5.6 Cyber costs $12.50 input / $75.00 output per million tokens (official OpenAI API price). 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 $20.00 for Claude Mythos 5 (267× as much) and $28.13 for GPT-5.6 Cyber (375× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. GPT-5.6 Cyber has not been scored yet, Laguna XS 2.1 has not been scored yet and Claude Mythos 5 has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for GPT-5.6 Cyber, Laguna XS 2.1 and Claude Mythos 5 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?
Claude Mythos 5 has the largest context window at 1,000,000 tokens, against 400,000 for GPT-5.6 Cyber and 262,144 for Laguna XS 2.1. Maximum output per response: GPT-5.6 Cyber up to 128,000, Laguna XS 2.1 up to 32,768, Claude Mythos 5 up to 128,000 tokens.
Which can read images, PDFs, audio or video?
GPT-5.6 Cyber accepts text and images; Laguna XS 2.1 accepts text; Claude Mythos 5 accepts text, images and PDFs. Claude Mythos 5 handles the widest range of inputs.
Are any of these open source?
Laguna XS 2.1 publishes its weights and can be self-hosted; GPT-5.6 Cyber and Claude Mythos 5 is proprietary.
Which is newer?
GPT-5.6 Cyber is the newest, released Aug 7, 2026. Laguna XS 2.1 came out Jul 2, 2026; Claude Mythos 5 came out Jun 9, 2026. Knowledge cutoff: GPT-5.6 Cyber Feb 16, 2026, Claude Mythos 5 Jan 31, 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.
