vakati-tech.ai

// head_to_head

Gemini 4 Carbon 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.

OpenRouter + LiveBenchAll comparisonsFull leaderboard

Gemini 4 Carbon scores higher, Gemma 4 31B costs less — it depends on your workload.

Gemini 4 Carbon is ahead by 51.1 points overall, and Gemma 4 31B lists 16× cheaper per blended million tokens. Whether 51.1 points is worth that depends on how much a wrong answer costs you. Gemma 4 31B also leads on measured cost per point of capability, at $0.0022 per point.

google

Gemini 4 Carbon

Blended / 1M
$4.00
Context
1.0M
Released
Nov 10, 2026
Overall score
83.5
reasoningtool callingaudio inputfile inputimage inputvideo inputprompt caching

google

Gemma 4 31B

Blended / 1M
$0.250
Context
262K
Released
Apr 2, 2026
Overall score
32.4
reasoningtool callingimage inputvideo input

Specs and pricing

MetricGemini 4 CarbonGemma 4 31B
LiveBench overall

Weighted mean of the seven LiveBench category scores, 0–100. Higher is better.

83.5win32.4
Cost per point

Measured benchmark spend divided by overall score — dollars per point of capability.

$0.0202$0.0022win
Blended price / 1M

3:1 input:output mix, the usual shape of production traffic.

$4.00$0.250win
Input price / 1M$2.00$0.200win
Output price / 1M$10.00$0.400win
Cached input / 1M

Price of an input token served from the prompt cache, where the provider publishes one.

$0.100—
Context window1.0Mwin262K
Max output tokens944Kwin16K

Benchmarks by category

An overall score averages away the thing you probably care about. These are the seven LiveBench categories scored separately — Gemini 4 Carbon on top, Gemma 4 31B below, both out of 100.

Long-horizon agentic
72.7
8.5
Coding
91.0
20.3
Reasoning
90.6
68.3
Browser
80.0
0.0
Data analysis
86.5
53.3
Research
92.5
38.1
Documents & design
80.2
56.5

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.

WorkloadGemini 4 CarbonGemma 4 31B
Support chatbot

1.2K in / 400 out × 200K requests

$1,143/mo$80.00/mo
RAG assistant

8K in / 600 out × 100K requests

$1,440/mo$184.00/mo
Coding agent

40K in / 4K out × 20K requests

$1,336/mo$192.00/mo
Document extraction

20K in / 1.5K out × 50K requests

$2,655/mo$230.00/mo
Bulk classification

500 in / 20 out × 5M requests

$5,050/mo$540.00/mo
Run these two through the cost calculator

Which should you pick?

You are running this at volume

Gemma 4 31B

Lowest measured cost per point of capability at $0.0022 per point — the gap compounds with every request.

Quality matters more than the bill

Gemini 4 Carbon

Highest overall LiveBench score of the two at 83.5.

The workload is coding or agentic work

Gemini 4 Carbon

Leads on long-horizon agentic — 72.7 against 8.5.

You need to fit large documents in one call

Gemini 4 Carbon

Wider context window — 1.0M against 262K.

Gemini 4 Carbon vs Gemma 4 31B FAQ

Which is better, Gemini 4 Carbon or Gemma 4 31B?

Gemini 4 Carbon scores higher, Gemma 4 31B costs less — it depends on your workload. Gemini 4 Carbon is ahead by 51.1 points overall, and Gemma 4 31B lists 16× cheaper per blended million tokens. Whether 51.1 points is worth that depends on how much a wrong answer costs you. Gemma 4 31B also leads on measured cost per point of capability, at $0.0022 per point.

Is Gemini 4 Carbon cheaper than Gemma 4 31B?

Gemma 4 31B is cheaper. On a 3:1 input:output blend, Gemini 4 Carbon lists at $4.00 per million tokens and Gemma 4 31B at $0.250 — Gemma 4 31B is 16× cheaper. Input and output are priced separately — Gemini 4 Carbon charges $2.00 in and $10.00 out, Gemma 4 31B charges $0.200 and $0.400 — so the model that looks cheaper flips depending on how output-heavy your workload is.

Gemini 4 Carbon vs Gemma 4 31B: which scores higher on benchmarks?

Gemini 4 Carbon scores 83.5 and Gemma 4 31B scores 32.4 overall on LiveBench, the weighted mean of its seven categories. That is a 51.1-point lead for Gemini 4 Carbon. Category scores differ from the overall figure — a model can lead on reasoning and trail on coding, which the per-category table above breaks out.

Which gives better value for money, Gemini 4 Carbon or Gemma 4 31B?

Gemma 4 31B. Cost per point divides the measured dollars LiveBench spent running the benchmark by the score it earned, so it captures something token pricing misses: a reasoning model can emit many times more tokens than its per-token rate suggests. Gemini 4 Carbon works out at $0.0202 per point and Gemma 4 31B at $0.0022.

Does Gemini 4 Carbon or Gemma 4 31B have a bigger context window?

Gemini 4 Carbon has the larger context window: 1.0M for Gemini 4 Carbon 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.

Do Gemini 4 Carbon and Gemma 4 31B support prompt caching?

Gemini 4 Carbon publishes a cached-input rate of $0.100 per million tokens against a full input rate of $2.00. The catalogue lists no separate cached rate for Gemma 4 31B, which means the provider does not price it separately here — not that caching is unavailable.

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 2026-11-06, 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.