Model comparison
Claude Fable 5 vs Claude Sonnet 5
Two Anthropic tiers compared on the figures that decide which one a workload actually needs.
Catalog record checked August 12, 2026; individual provider fields may change.
Anthropic
Claude Fable 5
Frontier
Anthropic
Claude Sonnet 5
Balanced
| Specification | Claude Fable 5 | Claude Sonnet 5 |
|---|---|---|
| Provider | Anthropic | Anthropic |
| Tier | Frontier | Balanced |
| Context window | 1M | 1M |
| Max output | 128K | 128K |
| Input / 1M tokens | $10 | Winner: $2 |
| Output / 1M tokens | $50 | Winner: $10 |
| Weights | Closed | Closed |
| Parameters | Not disclosed | Not disclosed |
| Reasoning levels | low, medium, high, xhigh, max | low, medium, high, xhigh, max |
| Modalities | text, image | text, image |
| Released | June 9, 2026 | June 30, 2026 |
| Artificial Analysis Intelligence Index [max] (2026-08-08) | Winner: 62 | 55 |
Prices are USD per million tokens at standard rates, excluding batch and caching discounts. Bold indicates the better figure where one is objectively better. Values we could not confirm from the provider are shown as “Not verified” rather than estimated.
Pricing tiers: Claude Sonnet 5: List price is $2/$10 per million input/output tokens. Anthropic made the launch intro rate the standard price; the previously scheduled 2026-09-01 step to $3/$15 will not occur.
Claude Fable 5
Anthropic's most expensive tier, at twice the price of Opus 5; access was suspended on 2026-06-12 and restored on 2026-07-01.
Best for
- Work where capability outweighs cost entirely
Watch out
Opus 5 scored higher on the Artificial Analysis Intelligence Index at half the price — justify the premium.
Claude Sonnet 5
Anthropic's mid tier on paper, though its DeepSWE cost per completed task is the highest recorded here.
Best for
- Everyday generation
- Short, well-scoped tasks
Watch out
Cheaper per token than Opus 5 but $26.40 per DeepSWE task against Opus 5's $11.84 — it used 214K output tokens over 268 steps.
Benchmark
DeepSWE 1.1 in context
Both models shown against the wider field, with cost per completed task alongside the score.
Local leader
Claude Opus 5 [max]
74%
Rows shown
24
Highest published reasoning effort per model (not best Pass@1)
Snapshot date
2026-08-13
Mirrored from deepswe.datacurve.ai
Better is toward the top-right (higher pass rate, lower cost). X-axis is reversed to match DeepSWE’s public chart. v1.1 uses average cost / tokens / steps; v1 uses published medians.
| # | Model | Pass@1 | Cost / task | Tokens / task | Steps / task |
|---|---|---|---|---|---|
| 1 | Claude Opus 5 [max] | 74% | $11.84 | 118k | 99 |
| 2 | GPT-5.6 Sol [max] | 73% | $8.39 | 60k | 61 |
| 3 | Claude Fable 5 [max] | 70% | $21.63 | 119k | 88 |
| 4 | GPT-5.6 Terra [max] | 70% | $4.95 | 72k | 76 |
| 5 | Kimi K3 [max] | 69% | $4.65 | 82k | 98 |
| 6 | GPT-5.6 Luna [max] | 67% | $3.03 | 73k | 102 |
| 7 | GPT-5.5 [xhigh] | 67% | $7.23 | 46k | 82 |
| 8 | Grok 4.6 [xhigh] | 67% | $5.50 | 71k | 87 |
| 9 | Gemini 3.7 Flash [high] | 65% | $2.18 | 107k | 125 |
| 10 | DeepSeek V4-Pro [max] | 63% | $0.24 | 106k | 155 |
| 11 | Claude Opus 4.8 [max] | 59% | $13.22 | 135k | 120 |
| 12 | Qwen3.8-Max [xhigh] | 58% | $3.73 | 95k | 111 |
| 13 | Muse Spark 1.2 [xhigh] | 55% | $3.70 | 99k | 101 |
| 14 | Claude Sonnet 5 [max] | 54% | $26.40 | 214k | 268 |
| 15 | Grok 4.5 [high] | 54% | $2.42 | 36k | 61 |
| 16 | DeepSeek V4-Flash [max] | 53% | $0.10 | 108k | 153 |
| 17 | Muse Spark 1.1 [xhigh] | 53% | $2.36 | 74k | 96 |
| 18 | GPT-5.4 [xhigh] | 52% | $5.65 | 71k | 70 |
| 19 | Gemini 3.6 Flash [high] | 47% | $4.42 | 96k | 117 |
| 20 | GLM 5.2 [max] | 44% | $3.92 | 78k | 129 |
| 21 | Gemini 3.5 Flash [high] | 36% | $3.45 | 76k | 105 |
| 22 | Kimi K2.7 Code | 31% | $2.82 | 59k | 149 |
| 23 | Claude Sonnet 4.6 [high] | 30% | $5.52 | 76k | 134 |
| 24 | Gemini 3.1 Pro [high] | 12% | $2.14 | 28k | 76 |
DeepSWE “Best” picks the highest published reasoning effort per model (not the highest pass rate). Small gaps may not be statistically meaningful — confirm on deepswe.datacurve.ai.
Common questions
Claude Fable 5 vs Claude Sonnet 5
Answered from the verified figures on this page rather than general guidance.
Is Claude Fable 5 or Claude Sonnet 5 cheaper for input?
Claude Sonnet 5 is cheaper at $2 per million input tokens, against $10 for Claude Fable 5 — roughly 5.0× the price. Output tokens usually dominate a real bill, so weigh the output rate more heavily than the input rate. These are base rates; Claude Sonnet 5 has tiered pricing: List price is $2/$10 per million input/output tokens. Anthropic made the launch intro rate the standard price; the previously scheduled 2026-09-01 step to $3/$15 will not occur.
Is Claude Fable 5 or Claude Sonnet 5 cheaper for output?
Claude Sonnet 5 is cheaper at $10 per million output tokens, against $50 for Claude Fable 5 — roughly 5.0× the price. Output tokens usually dominate a real bill, so weigh the output rate more heavily than the input rate. These are base rates; Claude Sonnet 5 has tiered pricing: List price is $2/$10 per million input/output tokens. Anthropic made the launch intro rate the standard price; the previously scheduled 2026-09-01 step to $3/$15 will not occur.
Which has the larger context window, Claude Fable 5 or Claude Sonnet 5?
Both accept about 1M tokens of context, so document length will not decide between them.
Do Claude Fable 5 and Claude Sonnet 5 support the same reasoning levels?
Yes — both accept the same effort settings: "low", "medium", "high", "xhigh", "max". Higher effort costs more and takes longer, so start low and raise it only where output quality actually improves.
Should I use Claude Fable 5 or Claude Sonnet 5?
Claude Fable 5 is the frontier tier and Claude Sonnet 5 the balanced tier. The useful question is whether your hardest task actually fails on the cheaper one — most production volume such as classification, extraction and summarisation does not.
Next step
Choosing between them
Tier and workload decide this more reliably than a leaderboard position does.
If both sit in the same tier, the decision usually comes down to context window and output price rather than headline capability — output tokens dominate real bills.
If one is a step up within the same provider, the useful question is whether your hardest task actually fails on the cheaper tier. Most production volume — classification, extraction, summarization — does not.
Choosing a harness rather than a model — Cursor, Copilot, Claude Code, Muse Code, or Lovable? Compare agentic harnesses · Latest releases.