Anthropic, released Sep 28, 2026
Claude Sonnet 5.5price, context, benchmarks and release details
- Input, per 1M tokens
- $2.00Anthropic models & pricingOfficial Claude API; lowest short-context on-demand tier; batch/cache/long-context rates excluded
Retrieved Oct 9, 2026 · factual citation
Open source ↗ - Output, per 1M tokens
- $10.00Anthropic models & pricingOfficial Claude API; lowest short-context on-demand tier; batch/cache/long-context rates excluded
Retrieved Oct 9, 2026 · factual citation
Open source ↗ - Context window
- 1MAnthropic models & pricingOfficial Claude API; lowest short-context on-demand tier; batch/cache/long-context rates excluded
Retrieved Oct 9, 2026 · factual citation
Open source ↗ - Max output
- 128KAnthropic models & pricingOfficial Claude API; lowest short-context on-demand tier; batch/cache/long-context rates excluded
Retrieved Oct 9, 2026 · factual citation
Open source ↗ - Released
- Sep 28, 2026Anthropic models & pricingOfficial Claude API; lowest short-context on-demand tier; batch/cache/long-context rates excluded
Retrieved Oct 9, 2026 · factual citation
Open source ↗
How the score breaks down
Weights: reasoning 30%, math 15%, coding 40%, preference 15%. Results use fixed 0–100 scales before averaging, and thin evidence is pulled toward 50. Method si-v3-retained-evidence-2, computed Oct 9, 2026, 05:13 UTC.
Around it on the leaderboard
- 13 GPT-5.4 69.7
- 14 GPT-5.5 69.5
- 15 Claude Sonnet 5.5 69.4
- 16 GPT-6 Sol 68.9
- 17 Gemini 3.8 Flash 68.8
Benchmark results
23 benchmarks, 24 resultsEach row shows the best published result. Where a model was tested at several settings, such as reasoning effort, open the row to see each one. Hover or tap a value for its source.
Open source ↗ 95.6
Open source ↗ 64.5
Open source ↗ 98.5
Open source ↗ 90.9
Open source ↗ 80.0
Open source ↗ 98.0
Open source ↗ 69.2
Open source ↗ 100.0
Open source ↗ 80.5
Open source ↗ 88.8
Open source ↗ 98.0
Open source ↗ 97.1
Open source ↗ 100.0
Open source ↗ 91.7
Open source ↗ 72.4
Open source ↗ 100.0
Open source ↗ 84.8
Open source ↗ 93.0
Open source ↗ 36.4
Open source ↗ 35.0
Open source ↗ 46.7
Terminal-Bench 4.0coding 70.6%Official model cards via models.devLab-reported; metric score; transcribed by MIT models.dev catalog; not independently evaluated [variant] 4.0Published Sep 28, 2026
Retrieved Oct 9, 2026 · factual citation; MIT transcription
Open source ↗
70.6 2 settings
- Setting 1 70.6%Official model cards via models.devLab-reported; metric score; transcribed by MIT models.dev catalog; not independently evaluated [variant] 4.0Published Sep 28, 2026
Retrieved Oct 9, 2026 · factual citation; MIT transcription
Open source ↗ - Setting 2 61.8%Terminal-BenchTerminal-Bench 4.0; published harness submission, 95% CI retained at source [variant] Claude Code; maxPublished Oct 6, 2026
Retrieved Oct 9, 2026 · Apache-2.0; factual citation
Open source ↗
Open source ↗ 79.5
Normalization uses a fixed 0–100 scale for each unit, independent of other models. Compare evaluation conditions before reading a small gap as decisive. “Lab-reported” marks the provider's own published figure.
Details and sources
- Open weights
- Nomodels.devPublished source fact
Retrieved Oct 9, 2026 · MIT
Open source ↗ - License
- not yet reported
- Input modalities
- text, image, pdfmodels.devPublished source fact
Retrieved Oct 9, 2026 · MIT
Open source ↗ - First seen by SuperIndex
- Oct 8, 2026
- Coverage
- 75% of expected source weight
Reported (5)
- Epoch AI BenchmarkingOct 8, 2026
- Official model cards via models.devOct 8, 2026
- LiveBenchOct 8, 2026
- LMArena / ArenaOct 8, 2026
- Terminal-BenchOct 8, 2026
Awaiting (2)
- ARC Prize13% of weight
- Humanity’s Last Exam13% of weight
Confidence rises as pending sources publish. Some sources never cover some models, so confidence reaches 100% at 80% of expected weight.