Claude Fable 5 vs Laguna XS.2 vs GPT-5.6 Sol
Too close to call on our weighted score (GPT-5.6 Sol 72, Claude Fable 5 72, Laguna XS.2 36). The right pick depends on what you value most.
AnthropicClaude Fable 5
72/100- ECI162.2
- Price$10.00 / $50.00
- Context1M
PoolsideLaguna XS.2
36/100- ECI—
- Price—
- Context262K
OpenAIGPT-5.6 Sol
72/100- ECI161.8
- Price$4.00 / $20.00
- Context1.05M
Too close to call
It is close. Our weighted score puts them within a point (GPT-5.6 Sol 72/100, Claude Fable 5 72/100, Laguna XS.2 36/100), so choose by what matters most for your work: GPT-5.6 Sol on price and GPT-5.6 Sol 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 priceGPT-5.6 SolGPT-5.6 Sol $8.00 · Claude Fable 5 $20.00 per 1M tokens (3:1 blend) · Laguna XS.2 unpriced
- Longest contextGPT-5.6 SolGPT-5.6 Sol 1,050,000 · Claude Fable 5 1,000,000 · Laguna XS.2 262,144 tokens
- Widest inputsClaude Fable 5 and GPT-5.6 SolClaude Fable 5: Text, Images, PDFs · Laguna XS.2: Text · GPT-5.6 Sol: Text, Images, PDFs
- Self-hostingLaguna XS.2Publishes downloadable weights
| Measure | Weight | Claude Fable 5 | Laguna XS.2 | GPT-5.6 Sol |
|---|---|---|---|---|
| Inputs & features | 60% | 80 | 35 | 80 |
| Context window | 40% | 60 | 37 | 61 |
| Overall | 100% | 72/100 | 36/100 | 72/100 |
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 Fable 5Anthropic | Laguna XS.2Poolside | GPT-5.6 SolOpenAI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 162.2 (best) | — | 161.8 |
| ECI rank | #7 of 148 (best) | — | #8 of 148 |
| GPQA DiamondGraduate-level science questions | 85.9% | — | 93.5% (best) |
| FrontierMath Tiers 1–3Research-level mathematics | 87.0% | — | 89.1% (best) |
| OTIS Mock AIME 2024–2025Competition mathematics | 100% | — | 100% |
| SimpleQA VerifiedShort factual questions | 70.7% (best) | — | 69.7% |
| Price per million tokens | |||
| Input | $10.00 | — | $4.00 (best) |
| Output | $50.00 | — | $20.00 (best) |
| Cached input | $1.00 | — | $0.40 (best) |
| Blended (3:1) | $20.00 | — | $8.00 (best) |
| Long-context rate | Same rate | — | Over 272K: $8.00 / $30.00 |
| Price source | Official Anthropic API | — | Official OpenAI API |
| Limits | |||
| Context window | 1,000,000 tokens | 262,144 tokens | 1,050,000 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 | Yes | No | Yes |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | Yeslow · medium · high · xhigh · max | Yes | Yeslow · medium · high · xhigh · max |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | No | Yes |
| Availability | |||
| Weights | Proprietary | Open | Proprietary |
| API model ID | claude-fable-5 | — | gpt-5.6-sol |
| API providers | 38 | 1 | 40 (best) |
| Released | Jun 9, 2026 | Apr 28, 2026 | Jul 9, 2026 |
| Knowledge cutoff | Jan 31, 2026 | — | Feb 16, 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 Fable 5$200.00
- Laguna XS.2—
- GPT-5.6 Sol$80.00
Which should you choose?
Which is better: Claude Fable 5, Laguna XS.2 or GPT-5.6 Sol?
It is close. Our weighted score puts them within a point (GPT-5.6 Sol 72/100, Claude Fable 5 72/100, Laguna XS.2 36/100), so choose by what matters most for your work: GPT-5.6 Sol on price and GPT-5.6 Sol 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, Claude Fable 5, Laguna XS.2 or GPT-5.6 Sol?
GPT-5.6 Sol is cheaper at $4.00 input / $20.00 output per million tokens (official OpenAI API price). Claude Fable 5 costs $10.00 input / $50.00 output per million tokens (official Anthropic API price). At a typical mix of three input tokens to one output token, that is $8.00 per million tokens for GPT-5.6 Sol versus $20.00 for Claude Fable 5 (2.5× as much). Laguna XS.2 has no published per-token price.
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Claude Fable 5 has an ECI of 162.2, Laguna XS.2 has not been scored yet and GPT-5.6 Sol has an ECI of 161.8.
Which is better for coding?
There are no published SWE-bench Verified results for Claude Fable 5, Laguna XS.2 and GPT-5.6 Sol 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-5.6 Sol has the largest context window at 1,050,000 tokens, against 1,000,000 for Claude Fable 5 and 262,144 for Laguna XS.2. Maximum output per response: Claude Fable 5 up to 128,000, Laguna XS.2 up to 32,768, GPT-5.6 Sol up to 128,000 tokens.
Which can read images, PDFs, audio or video?
Claude Fable 5 accepts text, images and PDFs; Laguna XS.2 accepts text; GPT-5.6 Sol accepts text, images and PDFs. Claude Fable 5 handles the widest range of inputs.
Are any of these open source?
Laguna XS.2 publishes its weights and can be self-hosted; Claude Fable 5 and GPT-5.6 Sol is proprietary.
Which is newer?
GPT-5.6 Sol is the newest, released Jul 9, 2026. Claude Fable 5 came out Jun 9, 2026; Laguna XS.2 came out Apr 28, 2026. Knowledge cutoff: Claude Fable 5 Jan 31, 2026, GPT-5.6 Sol Feb 16, 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.
