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Gemini 3.8 Flash 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 3.8 Flash scores higher, Gemma 4 31B costs less — it depends on your workload.

Gemini 3.8 Flash is ahead by 20.5 points overall, and Gemma 4 31B lists 6.6× cheaper per blended million tokens. Whether 20.5 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 $0.0017 per point.

google

Gemini 3.8 Flash

Blended / 1M
$1.65
Context
1.0M
Released
Sep 2, 2026
Overall score
59.2
reasoningtool callingimage inputvideo inputfile inputaudio inputprompt caching

google

Gemma 4 31B

Blended / 1M
$0.250
Context
262K
Released
Apr 2, 2026
Overall score
38.7
reasoningtool callingimage inputvideo input

Specs and pricing

MetricGemini 3.8 FlashGemma 4 31B
LiveBench overall

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

59.2win38.7
Cost per point

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

$0.0166$0.0017win
Blended price / 1M

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

$1.65$0.250win
Input price / 1M$0.825$0.200win
Output price / 1M$4.13$0.400win
Cached input / 1M

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

$0.083—
Context window1.0Mwin262K
Max output tokens66Kwin16K

Benchmarks by category

An overall score averages away the thing you probably care about. These are the seven LiveBench categories scored separately — Gemini 3.8 Flash on top, Gemma 4 31B below, both out of 100.

Long-horizon agentic
16.5
10.7
Coding
82.2
49.0
Reasoning
89.3
68.3
Browser
45.1
0.0
Data analysistoo close to call
54.0
53.3
Research
79.1
44.4
Documents & design
72.7
58.4

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 3.8 FlashGemma 4 31B
Support chatbot

1.2K in / 400 out × 200K requests

$474.54/mo$80.00/mo
RAG assistant

8K in / 600 out × 100K requests

$610.50/mo$184.00/mo
Coding agent

40K in / 4K out × 20K requests

$574.20/mo$192.00/mo
Document extraction

20K in / 1.5K out × 50K requests

$1,097/mo$230.00/mo
Bulk classification

500 in / 20 out × 5M requests

$2,104/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 $0.0017 per point — the gap compounds with every request.

Quality matters more than the bill

Gemini 3.8 Flash

Highest overall LiveBench score of the two at 59.2.

The workload is coding or agentic work

Gemini 3.8 Flash

Leads on long-horizon agentic — 16.5 against 10.7.

You need to fit large documents in one call

Gemini 3.8 Flash

Wider context window — 1.0M against 262K.

Gemini 3.8 Flash vs Gemma 4 31B FAQ

Which is better, Gemini 3.8 Flash or Gemma 4 31B?

Gemini 3.8 Flash scores higher, Gemma 4 31B costs less — it depends on your workload. Gemini 3.8 Flash is ahead by 20.5 points overall, and Gemma 4 31B lists 6.6× cheaper per blended million tokens. Whether 20.5 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 $0.0017 per point.

Is Gemini 3.8 Flash cheaper than Gemma 4 31B?

Gemma 4 31B is cheaper. On a 3:1 input:output blend, Gemini 3.8 Flash lists at $1.65 per million tokens and Gemma 4 31B at $0.250 — Gemma 4 31B is 6.6× cheaper. Input and output are priced separately — Gemini 3.8 Flash charges $0.825 in and $4.13 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 3.8 Flash vs Gemma 4 31B: which scores higher on benchmarks?

Gemini 3.8 Flash scores 59.2 and Gemma 4 31B scores 38.7 overall on LiveBench, the weighted mean of its seven categories. That is a 20.5-point lead for Gemini 3.8 Flash. 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 3.8 Flash 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 3.8 Flash works out at $0.0166 per point and Gemma 4 31B at $0.0017.

Does Gemini 3.8 Flash or Gemma 4 31B have a bigger context window?

Gemini 3.8 Flash has the larger context window: 1.0M for Gemini 3.8 Flash 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 3.8 Flash and Gemma 4 31B support prompt caching?

Gemini 3.8 Flash publishes a cached-input rate of $0.083 per million tokens against a full input rate of $0.825. 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 2026-10-25, 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.