C4ai Command R 08 2024 — Benchmarks

Benchmark scores for C4ai Command R 08 2024 aggregated from public leaderboards (4 boards), with how it ranks among open models and how it compares with its nearest neighbours. See hardware requirements for what you need to run it.

No llmrun Composite Score for C4ai Command R 08 2024: the score is only published for a model measured on at least 4 of the boards in the score panel, spanning at least 2 benchmark categories and including a reasoning or coding board — or on 3 boards that narrow its score to a 90% range of at most 15.6 points. Either way the results must come from at least two different benchmarks. Its individual scores below stand on their own — see how the llmrun Composite Score gets calculated.

C4ai Command R 08 2024 reasoning benchmarks

Multi-step logic and problem-solving — can the model think through a hard problem rather than recall an answer.

DTBench
DTBench is a set of handcrafted, expert-validated multiple-choice questions on decision theory — Newcomb-like problems and related puzzles — built by the Conceptual Reasoning Index project with Anthropic. It tests whether a model can follow an abstract argument rather than recall a fact.
46.4%
46.4%
median 68.7%open leader 93.9%
Overall best: Claude Opus 5.5 Max 98.9%
4th percentile · #67 of 70 open · #228 of 235 all
#67 of 70
#228 of 235
Lab-reported
Epoch AI · Aug 2024
LMCA
LMCA (Language Model Conceptual Argumentation) scores models on expert-rated arguments across philosophy, decision theory, and AI risk — domains where there is no empirical feedback loop to check an answer against. It is part of the Conceptual Reasoning Index, built with Anthropic by researchers Emery Cooper and Caspar Oesterheld.
9.2%
9.2%
median 25.5%open leader 55.5%
Overall best: Claude Sonnet 5.5 Max 68.3%
11th percentile · #50 of 56 open · #186 of 197 all
#50 of 56
#186 of 197
Lab-reported
Epoch AI · Aug 2024

C4ai Command R 08 2024 knowledge benchmarks

Breadth and depth of factual knowledge across many subjects, paired with the reasoning to apply it.

MMLU
MMLU (Massive Multitask Language Understanding) spans 57 subjects from history to law to medicine as multiple-choice questions. It is the long-standing default for broad knowledge and remains the most widely-reported general benchmark.
65.2%
65.2%
median 62.5%open leader 87.2%
Overall best: GPT 4o (Nov 20, 2024) 88.1%
56th percentile · #38 of 85 open · #78 of 136 all
#38 of 85
#78 of 136
Lab-reported
Epoch AI · Aug 2024

C4ai Command R 08 2024 instruction following benchmarks

How precisely a model obeys explicit, verifiable instructions — format, length, and content constraints — and, more broadly, how much people prefer its responses head-to-head in blind human voting.

LMArena Text
Arena (arena.ai, formerly LMArena / Chatbot Arena) ranks models by millions of blind, head-to-head human votes on real prompts. The Arena Score shown here is a Bradley-Terry rating computed with Arena's default style control, so higher means people preferred that model's answers more often — it's a relative rating, not a percentage, so compare it only to other models on this board.
1250.5
1250.5
median 1321.4open leader 1488.2
Overall best: Gemini 4 Carbon (High) 1527.5
30th percentile · #131 of 188 open · #315 of 408 all
#131 of 188
#315 of 408
Arena
Arena · Oct 2026

Scores aggregated from public benchmark sources (Epoch AI: 3 boards, Arena: 1 board) — each linked from the benchmark pages. llmrun does not run these benchmarks. Percentiles and ranks are among open models in the llmrun catalogue; the median and leader marks refer to the same field.