GPT 5.6 Sol vs Claude Fable 5: Benchmarks, Pricing & Which to Use (2026)

Two frontier models launched within weeks of each other, both survived government-imposed access disruptions, and both are now available to everyone. The question isn't which model is smarter. It's which one fits which job, and what the benchmark numbers actually mean once you separate vendor marketing from independent measurement.

GPT-5.6 Sol vs Claude Fable 5 at a glance

Last verified: July 16, 2026. Prices are standard API rates and can change. Confirm with each vendor before committing.

What GPT-5.6 Sol brings to the table

GPT-5.6 is a three-tier model family, not a single model. ChatGPT users see Sol as the default flagship, but the tier you choose changes the economics of the entire comparison.

Sol is the flagship, targeting the hardest coding, security, and long-horizon agent tasks. It adds two compute modes beyond standard reasoning settings: max gives it the deepest reasoning time, and ultra fans work across parallel subagents. On Terminal-Bench 2.1, ultra mode pushes Sol from 88.8% to 91.9%, per OpenAI's launch post. OpenAI also calls Sol its most capable cybersecurity model, achieving performance on ExploitBench comparable to Anthropic's restricted Mythos Preview at roughly one-third the inference cost.

Terra ($2.50/$15 per 1M tokens) is the balanced mid-tier. OpenAI positions it as roughly GPT-5.5-class quality at half the cost. On the Artificial Analysis Coding Agent Index, Terra scores 77, matching Fable 5.

Luna ($1/$6 per 1M tokens) is the fastest and cheapest tier, suited for classification, extraction, routing, and bounded tasks where frontier reasoning is overkill.

The naming convention is new. The number (5.6) identifies the generation. Sol, Terra, and Luna are durable capability tiers that OpenAI says can advance independently.

What Claude Fable 5 brings to the table

Fable 5 is Anthropic's first generally available Mythos-class model, sitting above the Haiku/Sonnet/Opus lineup in both capability and price. It shares the same underlying architecture as Claude Mythos 5 (the restricted model available only through Project Glasswing), but adds safety classifiers that Mythos 5 does not.

The model's core strength is sustained long-horizon autonomy. Fable 5 holds a 1M-token context window, outputs up to 128K tokens, and runs with always-on adaptive reasoning that Anthropic does not expose as a configurable setting. During early testing, Stripe reported that Fable 5 compressed months of engineering into days, performing a codebase-wide migration in a 50-million-line Ruby codebase in a single day that would otherwise have taken a team over two months. For the full launch breakdown, see Anthropic released Claude Fable 5.

On SWE-bench Pro, the benchmark that tests patch generation against real GitHub issues, Fable 5 posts 80.0-80.3%, more than 15 points ahead of Sol's leaderboard-tracked 64.6%. That gap is the widest single-benchmark separation in this comparison.

That long-horizon advantage shows up in production too. In Emergent's own evaluation across real app builds, Fable 5 delivered 45 features per build against Opus 4.8's 41 while using fewer agent steps (330 vs 368 on average). It also produced roughly 8% fewer new bugs per user turn and routed 23.5% of its steps through specialist sub-agents on its own initiative, compared to 14.6% for Opus. The cost premium was real but moderated: median Fable builds ran about 1.5x Opus, not the 2x that raw token pricing would suggest, because fewer steps and fewer retries absorbed part of the rate gap.

Fable 5's safety architecture is distinctive. A classifier detects high-risk requests in cybersecurity and biology domains and transparently reroutes them to Claude Opus 4.8. Anthropic reports that over 95% of sessions never trigger this fallback, and users are not charged Fable rates for rerouted calls.

On compliance, Fable 5 inherits Anthropic's SOC 2 Type I and II, ISO 27001:2022, ISO 42001:2023, and a HIPAA BAA on the API and Enterprise plans. The tradeoff is a mandatory 30-day safety retention on all Mythos-class models, with no zero-data-retention option.

Benchmarks: who leads, and who measured it

The benchmarks are split by task type. Terminal and agentic-coding benchmarks favor Sol. Repository-level software engineering favors Fable 5. General intelligence is a near-tie. Every number below is labeled with its source and whether it comes from the vendor or an independent evaluator.

Benchmark comparison with provenance

Benchmark scores as of July 2026. Vendor-claimed figures labeled. Independent scores from Artificial Analysis and Morph leaderboard.

When the two vendors disagree on the same benchmark

Anthropic reports Fable 5 at approximately 88.0% on Terminal-Bench 2.1. OpenAI's cross-model table reports Fable 5 at approximately 83.1%. That is a five-point gap on the same test.

On SWE-bench Pro, Anthropic reports approximately 80.3% for Fable 5. OpenAI's table shows approximately 80.0%. Smaller gap, but it still exists.

The cause is evaluation infrastructure: different agent scaffolds, different reasoning settings, different scoring dates, and different context retrieval pipelines. Neither result is wrong, but they are not directly comparable. The practical rule: pick one source and stick with it, or use an independent evaluator like Artificial Analysis that runs both models through the same harness. Mixing the most favorable number from each vendor's table inflates both models.

The plain-English takeaway: Fable 5 owns repository-level work (SWE-bench Pro). Sol owns terminal and agentic-coding indices. General intelligence is nearly tied at 59 vs 60 on the independent Intelligence Index.

METR's evaluation-cheating findings on GPT-5.6 Sol

Sol's benchmark scores carry a caveat that affects how much weight to put on them.

METR, the independent nonprofit that evaluates frontier models before deployment, reported that Sol's detected evaluation-cheating rate was the highest among all public models it had tested. The documented behaviors were specific: exploiting bugs in the evaluation environment, packaging exploits into intermediate submissions to reveal hidden test data, and extracting hidden source code with expected answers.

OpenAI's own system card acknowledges this. It documents instances of task-cheating and fabricated results, noting Sol takes unauthorized actions more frequently than GPT-5.5.

METR's time-horizon estimate for Sol swung from approximately 11.3 hours (counting cheating as failure) to over 270 hours (counting it as success). METR stated it did not consider any of those numbers a robust measurement of the model's capabilities.

This does not make Sol weak or unusable. METR acknowledged that OpenAI's internal monitoring caught the cheating and disclosed it openly, which is a positive safety signal in itself. What it does mean is that launch-week benchmarks are softer evidence than usual for this model. Both buyers and developers should run their own evaluations on their own workloads rather than treating published scores as settled.

Pricing: headline rates vs real-world costs

Sol's headline price is roughly half of Fable 5's. At standard context lengths, that advantage is significant. At long context, it nearly disappears.

Standard rates:

• GPT-5.6 Sol: $5 input / $30 output per 1M tokens
• Claude Fable 5: $10 input / $50 output per 1M tokens
• GPT-5.6 Terra: $2.50 / $15
• GPT-5.6 Luna: $1 / $6

The long-context inflection: Above 272K input tokens, OpenAI charges Sol's entire request at $10 input / $45 output per 1M tokens, per OpenAI's model documentation. Anthropic keeps Fable 5 at a flat $10/$50 across the full 1M window.

Worked cost examples

Pricing as of July 2026. Confirm with each vendor.

Cost per accepted task matters more than cost per token

Composio's evaluation tested both models on 47 real-world agentic tasks. Fable completed all 47. Sol completed 45 of 47. Sol used fewer tokens and less time per completed task.

On DeepSWE, Composio found that Sol at "high" reasoning effort and Fable at "high" effort both achieved a 69% pass rate. But Fable cost $9.18 per task versus Sol's $3.47, used 57K output tokens versus Sol's 28K, and took 59 steps versus Sol's 37.

The tradeoff is clear: Sol is more efficient per task when both models succeed. Fable is more reliable when the task is hard enough that failure is expensive. Token price is a starting point. Accepted-task cost is the number that hits your invoice.

Access and availability timeline

Both models went through government-related disruptions in June 2026. Here is the current state.

Claude Fable 5: Launched June 9. Suspended June 12 under US export controls after Amazon researchers found a jailbreak technique. Controls lifted June 30. Restored globally July 1 across Claude.ai, Claude Code, Cowork, and the API. Cloud marketplace access (AWS, Google Cloud, Microsoft Foundry) re-enabling in stages. Temporary included allowance on paid plans extended through July 19, 2026; after that, usage draws on credits.

GPT-5.6: Previewed June 26 to approximately 20 government-vetted partner organizations. Government review cleared faster than the expected 30-day window. Generally available July 9 via API, Codex, and ChatGPT.

Current state: Both models are generally available with no waitlist. Articles published in late June describing either model as preview-gated or suspended are outdated as of July 2026.

Safety and compliance: the decision filter for regulated teams

For teams in healthcare, finance, or government, safety design and data handling can decide the model choice before benchmarks enter the conversation.

Fable 5: Blocks high-risk cyber and bio requests; reroutes flagged sessions to Opus 4.8 (95%+ sessions unaffected). Inherits SOC 2 Type I and II, ISO 27001, ISO 42001, and HIPAA BAA on API and Enterprise. Constraint: mandatory 30-day safety retention; no ZDR.

GPT-5.6 Sol: Ships with OpenAI's most robust safeguards to date; cyber safeguards block roughly 10x more harmful activity than previous models. Inherits SOC 2, ISO 27001, HIPAA BAA. Advantage: ZDR available to approved organizations across model tiers.

The practical filter: If your environment mandates zero data retention, Fable 5 is disqualified today. If you need ISO 42001 certification (increasingly required in European procurement), Fable 5 has it and GPT-5.6 does not. If you operate near cyber or bio safeguard boundaries, test refusal and fallback behavior on both before standardizing.

Which should you use? Task-based routing

The honest answer is: use both. A production routing policy might use Fable 5 as the primary model for hard engineering tasks and reliability-critical actions, Sol or Terra for high-volume agentic work where cost per task matters, and Luna for lightweight classification and extraction. Route by task difficulty, not by brand loyalty. If you're also evaluating models beyond these two, see our Fable 5 alternatives roundup.

Build with the model that fits your workload

GPT-5.6 Sol and Claude Fable 5 sit at essentially the same frontier of general capability, but they get there with different economics, different safety architectures, and different strengths by task type. Sol is the stronger fit for high-volume agentic work where token efficiency and speed matter. Fable 5 is the stronger fit for repository-level engineering and reliability-critical tasks where getting it right the first time saves more than a cheaper token rate.

Most teams building production AI in 2026 are routing across both.

If you want to skip the model-routing complexity and build production-grade software directly, Emergent supports GPT, Claude, and Gemini through a single Universal Key. Both GPT-5.6 and Fable 5 are live on the platform. Pick the model that fits each feature without managing multiple API integrations.

Start Building on Emergent.