// head_to_head
Command R (08-2024) vs Gemma 4 31B
Published LiveBench scores across all seven categories, live list pricing, context windows, and the measured cost of a point of capability — for both models, side by side.
Command R (08-2024) and Gemma 4 31B are priced within ~10% of each other.
Command R (08-2024) does not have a published LiveBench run, so this comparison covers price, context and declared capabilities only. A missing score means "not evaluated", not "worse" — the right way to separate these two is an eval on your own workload.
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Command R (08-2024)
- Blended / 1M
- $0.262
- Context
- 128K
- Released
- Aug 30, 2024
- Overall score
- Not evaluated
Gemma 4 31B
- Blended / 1M
- $0.250
- Context
- 262K
- Released
- Apr 2, 2026
- Overall score
- 38.7
Specs and pricing
| Metric | Command R (08-2024) | Gemma 4 31B |
|---|---|---|
| LiveBench overall Weighted mean of the seven LiveBench category scores, 0–100. Higher is better. | — | 38.7 |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | — | $0.0017 |
| Blended price / 1M 3:1 input:output mix, the usual shape of production traffic. | $0.262 | $0.250 |
| Input price / 1M | $0.150win | $0.200 |
| Output price / 1M | $0.600 | $0.400win |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | — | — |
| Context window | 128K | 262Kwin |
| Max output tokens | 4K | 16Kwin |
Benchmarks by category
An overall score averages away the thing you probably care about. These are the seven LiveBench categories scored separately — Command R (08-2024) on top, Gemma 4 31B below, both out of 100.
What each one costs to run
Per-token prices are hard to feel. These are monthly list costs for both models across five workload shapes, using each provider's published cached-input rate where there is one.
| Workload | Command R (08-2024) | Gemma 4 31B |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $84.00/mo | $80.00/mo |
| RAG assistant 8K in / 600 out × 100K requests | $156.00/mo | $184.00/mo |
| Coding agent 40K in / 4K out × 20K requests | $168.00/mo | $192.00/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $195.00/mo | $230.00/mo |
| Bulk classification 500 in / 20 out × 5M requests | $435.00/mo | $540.00/mo |
Which should you pick?
You need to fit large documents in one call
Gemma 4 31B
Wider context window — 262K against 128K.
Command R (08-2024) vs Gemma 4 31B FAQ
Which is better, Command R (08-2024) or Gemma 4 31B?
Command R (08-2024) and Gemma 4 31B are priced within ~10% of each other. Command R (08-2024) does not have a published LiveBench run, so this comparison covers price, context and declared capabilities only. A missing score means "not evaluated", not "worse" — the right way to separate these two is an eval on your own workload.
Is Command R (08-2024) cheaper than Gemma 4 31B?
They cost about the same. Both land near $0.262 per million tokens on a 3:1 input:output blend, so price is unlikely to be the deciding factor between them.
Does Command R (08-2024) or Gemma 4 31B have a bigger context window?
Gemma 4 31B has the larger context window: 128K for Command R (08-2024) against 262K for Gemma 4 31B. Note that a window you can fill is not a window you should fill — retrieval quality usually degrades well before the limit, and you pay for every token you put in it.
Related comparisons
How these numbers are produced
- Price — provider list price from OpenRouter, refreshed every 15 minutes. “Blended” is a 3:1 input:output mix.
- Scores — LiveBench release 2026-10-25, using their own category map. Each model shows its strongest published run. A blank means “not evaluated”, never “bad”.
- Cost per point — the measured dollars LiveBench spent on the run, divided by the score it earned.
- “Win” — awarded only past a threshold: one full point on a benchmark score, 10% on a price, 25% on a context window. Anything tighter reports as a tie, because effort settings alone move a LiveBench score by more than that.
Published benchmarks rank models on someone else's tasks. Before committing, see LLM & agent evaluation for building an eval on your own.