head-to-head
| Metric | Claude Fable 5 | Claude Sonnet 5 |
|---|---|---|
| SWE-bench Verified | 95.0% | 85.2% |
| SWE-bench Pro | 80.3% | 63.2% |
| Terminal-Bench | — | 80.4% (TB2.1) |
| Input $ / 1M | $10 | $2 |
| Output $ / 1M | $50 | $10 |
| Context | 1M | 1M |
| Open weights | No | No |
| Access | API · Claude Code · Claude Cowork · claude.ai | API · Claude Code · claude.ai (Free/Pro default) |
| Maker | Anthropic | Anthropic |
what do the benchmarks actually say?
On SWE-bench Verified — real, human-validated GitHub issues resolved end-to-end — Claude Fable 5 posts 95.0% against 85.2% for Claude Sonnet 5, a 9.8-point gap. Verified is the closest public proxy for "can it fix a real bug in a real repo without help", which is why it anchors our ranking.
SWE-bench Pro is the harder, less-saturated test — bigger repos, multi-file changes, no memorized answers. Here Claude Fable 5 leads with 80.3% to 63.2%, a 17.1-point margin.
A few points either way is real but not decisive: within that band, the agent scaffolding around the model — how it retrieves files, runs tests, and retries — often matters as much as the base model. Treat the gap as a lean, not a verdict.
which is cheaper to run?
Claude Sonnet 5 is the cheaper model: $2 per 1M input tokens ($10 output) versus $10 ($50 output) for Claude Fable 5 — roughly 5× less on input. Coding workloads are output-heavy — agents write diffs, tests and retries — so weight the output rate more than the input rate when you estimate a monthly bill.
when to pick each
Mythos-class flagship for long-horizon agentic runs: the model to reach for when a task spans hours and hundreds of tool calls and has to actually finish.
The best closed-model value — near-Opus scores at ~2.5× less, and the default daily driver for most developers.
how were these scores verified?
We only print a number once it's confirmed against a primary source or an independent evaluation, and each row on our leaderboard records which kind it is:
- Claude Fable 5: Independent (vals.ai, Jul 14 2026, mini-swe-agent bash-only harness): SWE-bench Verified 95.00% ±0.98. Held the top score until GPT-5.6 Sol was evaluated at 96.20% ±0.86 on the same harness — a 1.2-point gap that is inside the combined margin of error (~0.9 sigma), so the two are a statistical tie and we rank Sol first only because it scored higher. SWE-bench Pro 80.3% uses Anthropic's own scaffolding and is contested. Restored Jul 1, 2026 after a 20-day export-control suspension. Pricing $10/$50 per 1M.
- Claude Sonnet 5: Vendor-reported (Anthropic), on Anthropic's own scaffold. Independent comparison: vals.ai's bash-only harness measures Sonnet 5 at 79.6% ±1.80, 5.6 points lower — a gap that reflects the harness as much as the model, so treat the 85.2% as a best-case number. Intro pricing $2/$10 per 1M through Aug 31, 2026, then $3/$15.
Full reviewsClaude Fable 5, decodedClaude Sonnet 5, decoded
Ranked on our AI Coding Leaderboard, updated 2026-07-17. Scores are confirmed against primary sources; prices are per 1M input tokens and can change.
- AnthropicGENZ TECH — Claude Fable 5 returns — Independent (vals.ai, Jul 14 2026, mini-swe-agent bash-only harness): SWE-bench Verified 95.00% ±0.98. Held the top score until GPT-5.6 Sol was evaluated at 96.20% ±0.86 on the same harness — a 1.2-point gap that is inside the combined margin of error (~0.9 sigma), so the two are a statistical tie and we rank Sol first only because it scored higher. SWE-bench Pro 80.3% uses Anthropic's own scaffolding and is contested. Restored Jul 1, 2026 after a 20-day export-control suspension. Pricing $10/$50 per 1M.
- AnthropicGENZ TECH — Claude Sonnet 5, decoded — Vendor-reported (Anthropic), on Anthropic's own scaffold. Independent comparison: vals.ai's bash-only harness measures Sonnet 5 at 79.6% ±1.80, 5.6 points lower — a gap that reflects the harness as much as the model, so treat the 85.2% as a best-case number. Intro pricing $2/$10 per 1M through Aug 31, 2026, then $3/$15.
- BenchmarkSWE-bench — the real-GitHub-issue benchmark