Is Command R (08-2024) a good deal?
Whether anything in this catalogue beats Command R (08-2024) on both quality and price, and what you give up if it does. A computation on the current catalogue, not an opinion.
13 undominated of 136 · Sep 23, 2026
As of Sep 23, 2026, Command R (08-2024) is dominated for Balanced on LMArena. GLM 5.3 Flash scores 284.6 higher and costs 10% less, with a covering envelope. 13 of 136 rated, priced standard models are undominated.
Inspect model evidence Compare differences & requirements
GLM 5.3 Flash is both better and cheaper than Command R (08-2024): 284.6 points higher on LMArena and 10% less per million tokens, $0.02 cheaper at this mix.
LMArena Elo · higher is better
Scale starts at 1040 Elo
1187.3
Effective $/M · Balanced · lower is better
$0.262/M
LMArena Elo · higher is better
Scale starts at 1040 Elo
1471.9
Effective $/M · Balanced · lower is better
$0.237/M
Command R (08-2024) takes text, returns up to 4,000 tokens from a 128,000-token context, and is listed by 1 seller.
Compared against 136 rated, priced models on this lens: 23 models dominate it and give up nothing, 3 more dominate it but give something up. Command R (08-2024) scores 1187.3 at $0.26 per million tokens for this mix.
Envelope-safe replacements
Each row scores at least as high, costs no more, and covers this model’s context, output, modalities, tools, and reasoning. A cheaper narrower model is not listed here.
| Model | LMArena | Effective $/M | You save |
|---|---|---|---|
| GLM 5.3 Flash | 1471.9 +284.6 | $0.237/M | 10% |
| Gemma 4 31B | 1441.7 +254.4 | $0.153/M | 42% |
| Hy3 | 1440.6 +253.3 | $0.144/M | 45% |
| Gemma 4 26B A4B | 1434.5 +247.2 | $0.143/M | 46% |
| DeepSeek V4 Flash 0423 | 1431.8 +244.5 | $0.103/M | 61% |
| MiMo-V2.5 | 1427.4 +240.1 | $0.175/M | 33% |
| Qwen3 235B A22B Instruct 2507 | 1419.8 +232.5 | $0.153/M | 42% |
| Step 3.5 Flash | 1403.7 +216.4 | $0.150/M | 43% |
| Qwen3.5-Flash | 1397.7 +210.4 | $0.114/M | 57% |
| Qwen3 30B A3B Instruct 2507 | 1383.6 +196.3 | $0.084/M | 68% |
| Solar Pro 4 | 1377.3 +190.0 | $0.158/M | 40% |
| gpt-oss-120b | 1365.9 +178.6 | $0.262/M | 0% |
| Gemma 3 27B | 1357.7 +170.4 | $0.172/M | 34% |
| GLM 4.7 Flash | 1352.2 +164.9 | $0.145/M | 45% |
| Qwen3 32B | 1340.0 +152.7 | $0.130/M | 50% |
| Gemma 3 12B | 1334.2 +146.9 | $0.075/M | 71% |
| GPT-5 Nano | 1319.7 +132.4 | $0.138/M | 48% |
| Qwen3 30B A3B | 1317.0 +129.7 | $0.215/M | 18% |
| Granite 4.2 8B | 1316.8 +129.5 | $0.107/M | 59% |
| gpt-oss-20b | 1287.3 +100.0 | $0.036/M | 86% |
| GPT-4o-mini (2024-07-18) | 1286.3 +99.0 | $0.262/M | 0% |
| GPT-4.1 Nano | 1284.7 +97.4 | $0.175/M | 33% |
| Llama 3.3 70B Instruct | 1274.0 +86.7 | $0.155/M | 41% |
Higher score, lower price, named losses
Not a drop-in. The loss is why these are not a recommendation.
| Model | LMArena | Effective $/M | You give up |
|---|---|---|---|
| Gemma 3 4B | 1290.7 | $0.063/M | tools |
| Mistral Small 3 | 1233.5 | $0.058/M | context, tools |
| Phi 4 | 1216.6 | $0.088/M | context, tools |
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Saved decision references
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Constraint: replacements must preserve the model’s capabilities; any losses remain named trade-offs.
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