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Claude Instant 1.0

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Claude Instant 1.0 is a closed-weights language model from Anthropic, released March 2023. Licence: proprietary.

Catalogue identity notes

Anthropic API model claude-instant-v1.0 (later id claude-instant-1.0). Anthropic launched Claude and Claude Instant on 2023-03-14 (https://www.anthropic.com/news/introducing-claude); the archived API reference of 2023-03-15 says claude-instant-v1 'currently points at "claude-instant-v1.0"'. Retired 2024-11-06 (Anthropic model deprecations page). Comparison-only.

Model information sources

Release date ↗

49.8±3.0comparison only: no current run option verifiedfrom 2 benchmark results across 1 source* thinly tested, treat the rank as provisional

Score within the LMArena (text) comparison set only.

$ how it compares

The LLMs ranked either side of Claude Instant 1.0, out of 1182 with a score, then more from Anthropic.

#modelscorewhereRAMreleased
619XwinCoder-34B Xwin-LM50±1local~23GB2023-11
620Code Millenials 34B Bud Ecosystem50±1local~23GB2024-01
621Claude Instant 1.0 Anthropic50±3comparison only2023-03
622LFM2.5 1.2B Thinking Liquid AI50±2local~1.3GB2026-01
623StarChat2 15B v0.1 Hugging Face50±1local~12GB2024-03
more from Anthropic
2Claude Opus 5.5 Anthropic85±1api $8.00/M2026-09
4Claude Fable 5.1 Anthropic83±1api $20.00/M2026-09
6Claude Sonnet 5.5 Anthropic82±1api $4.00/M2026-09

see all LLMs · find the ones that fit your machine

$ what it's good at

math▁▂▃▆█▇▅▇▃▁▁▁ #303 of 526 * · 42% of its score · 1 test

Beats Command A (03-2025), GPT-4o (2024-05-13), GPT-4o (2024-11-20) here, though they rank higher overall. Trails DeepSeek R1 Distill Qwen 1.5B, Gemma 7B (base), CodeGemma 7B (v1.0) here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
GSM8K70.8% ✎▂▂▂▂▃▄▅▇██▆▄ beats 120/200easy · avg 69%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 42%sharp · at its level · noisy
other tests1 not in a task · 54% of its score
testresultvs the others in its rundifficultycountswhy
BIG-Bench Hard66.9% ✎▁▁▁▁▂▄▅▆█▃▃▁ beats 105/155medium · avg 69%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 54%sharp · at its level · small run

$ where to run it

No verified API or download recorded yet · add one

$ how the score was worked out · Chain-of-Thought Hub: A Conti… 96%

Each result implies a score on its own, after adjusting for how hard its test is and who ran it. The score is their weighted average, with a small pull toward the middle for thinly tested models. Each test's weight is its counts column above; here it's added up by who ran the tests. How it works

sourceresultsshare of the scorelicence
Chain-of-Thought Hub: A Continuous Effort to Measure Large Language Models' Reasoning Performance — authors' own runs2CC BY 4.0
prior · pull toward the middle
weighted average= 49.8

$ improve this page

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Use a short GitHub form or edit the data through a pull request. Every number needs a source; here's which ones we can use.

data updated · sources and licences · json