GPT-5.5 Pro vs Laguna XS.2 vs Claude Fable 5
Too close to call on our weighted score (GPT-5.5 Pro 72, Claude Fable 5 72, Laguna XS.2 36). The right pick depends on what you value most.
OpenAIGPT-5.5 Pro
72/100- ECI162.4
- Price$30.00 / $180.00
- Context1.05M
PoolsideLaguna XS.2
36/100- ECI—
- Price—
- Context262K
AnthropicClaude Fable 5
72/100- ECI162.2
- Price$10.00 / $50.00
- Context1M
Too close to call
It is close. Our weighted score puts them within a point (GPT-5.5 Pro 72/100, Claude Fable 5 72/100, Laguna XS.2 36/100), so choose by what matters most for your work: Claude Fable 5 on price and GPT-5.5 Pro 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 priceClaude Fable 5Claude Fable 5 $20.00 · GPT-5.5 Pro $67.50 per 1M tokens (3:1 blend) · Laguna XS.2 unpriced
- Longest contextGPT-5.5 ProGPT-5.5 Pro 1,050,000 · Claude Fable 5 1,000,000 · Laguna XS.2 262,144 tokens
- Widest inputsGPT-5.5 Pro and Claude Fable 5GPT-5.5 Pro: Text, Images, PDFs · Laguna XS.2: Text · Claude Fable 5: Text, Images, PDFs
- Self-hostingLaguna XS.2Publishes downloadable weights
| Measure | Weight | GPT-5.5 Pro | Laguna XS.2 | Claude Fable 5 |
|---|---|---|---|---|
| Inputs & features | 60% | 80 | 35 | 80 |
| Context window | 40% | 61 | 37 | 60 |
| 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 | GPT-5.5 ProOpenAI | Laguna XS.2Poolside | Claude Fable 5Anthropic |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 162.4 (best) | — | 162.2 |
| ECI rank | #6 of 148 (best) | — | #7 of 148 |
| GPQA DiamondGraduate-level science questions | 93.9% (best) | — | 85.9% |
| FrontierMath Tiers 1–3Research-level mathematics | 87.7% (best) | — | 87.0% |
| OTIS Mock AIME 2024–2025Competition mathematics | 100% | — | 100% |
| SimpleQA VerifiedShort factual questions | — | — | 70.7% |
| Price per million tokens | |||
| Input | $30.00 | — | $10.00 (best) |
| Output | $180.00 | — | $50.00 (best) |
| Cached input | — | — | $1.00 |
| Blended (3:1) | $67.50 | — | $20.00 (best) |
| Long-context rate | Over 272K: $60.00 / $270.00 | — | Same rate |
| Price source | Official OpenAI API | — | Official Anthropic API |
| 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 | Yesmedium · high · xhigh | Yes | Yeslow · medium · high · xhigh · max |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | No | Yes |
| Availability | |||
| Weights | Proprietary | Open | Proprietary |
| API model ID | gpt-5.5-pro | — | claude-fable-5 |
| API providers | 19 | 1 | 38 (best) |
| Released | Apr 23, 2026 | Apr 28, 2026 | Jun 9, 2026 |
| Knowledge cutoff | Dec 1, 2025 | — | 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-5.5 Pro$660.00
- Laguna XS.2—
- Claude Fable 5$200.00
Which should you choose?
Which is better: GPT-5.5 Pro, Laguna XS.2 or Claude Fable 5?
It is close. Our weighted score puts them within a point (GPT-5.5 Pro 72/100, Claude Fable 5 72/100, Laguna XS.2 36/100), so choose by what matters most for your work: Claude Fable 5 on price and GPT-5.5 Pro 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-5.5 Pro, Laguna XS.2 or Claude Fable 5?
Claude Fable 5 is cheaper at $10.00 input / $50.00 output per million tokens (official Anthropic API price). GPT-5.5 Pro costs $30.00 input / $180.00 output per million tokens (official OpenAI API price). At a typical mix of three input tokens to one output token, that is $20.00 per million tokens for Claude Fable 5 versus $67.50 for GPT-5.5 Pro (3.4× 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. GPT-5.5 Pro has an ECI of 162.4, Laguna XS.2 has not been scored yet and Claude Fable 5 has an ECI of 162.2.
Which is better for coding?
There are no published SWE-bench Verified results for GPT-5.5 Pro, Laguna XS.2 and Claude Fable 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?
GPT-5.5 Pro 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: GPT-5.5 Pro up to 128,000, Laguna XS.2 up to 32,768, Claude Fable 5 up to 128,000 tokens.
Which can read images, PDFs, audio or video?
GPT-5.5 Pro accepts text, images and PDFs; Laguna XS.2 accepts text; Claude Fable 5 accepts text, images and PDFs. GPT-5.5 Pro handles the widest range of inputs.
Are any of these open source?
Laguna XS.2 publishes its weights and can be self-hosted; GPT-5.5 Pro and Claude Fable 5 is proprietary.
Which is newer?
Claude Fable 5 is the newest, released Jun 9, 2026. Laguna XS.2 came out Apr 28, 2026; GPT-5.5 Pro came out Apr 23, 2026. Knowledge cutoff: GPT-5.5 Pro Dec 1, 2025, Claude Fable 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.
