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Gemini 3.5 Flash vs Gemini 3.7 Flash

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 3.7 Flash wins outright — it scores higher and costs less.

Gemini 3.7 Flash leads by 4.2 points overall while listing 4.5× cheaper per blended million tokens. There is no trade-off to reason about here; the only reason to pick the other is a constraint this table does not show, like an existing contract, a region, or a provider you are already on. Gemini 3.7 Flash also leads on measured cost per point of capability, at $0.0875 per point.

google

Gemini 3.5 Flash

Blended / 1M
$3.38
Context
1.0M
Released
May 19, 2026
Overall score
74.6
reasoningtool callingimage inputvideo inputfile inputaudio inputprompt caching

google

Gemini 3.7 Flash

Blended / 1M
$0.750
Context
1.0M
Released
Aug 13, 2026
Overall score
78.8
reasoningtool callingimage inputvideo inputfile inputaudio inputprompt caching

Specs and pricing

MetricGemini 3.5 FlashGemini 3.7 Flash
LiveBench overall

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

74.678.8win
Cost per point

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

$0.1357$0.0875win
Blended price / 1M

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

$3.38$0.750win
Input price / 1M$1.50$0.375win
Output price / 1M$9.00$1.88win
Cached input / 1M

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

$0.150$0.037win
Context window1.0M1.0M
Max output tokens66K66K

Benchmarks by category

An overall score averages away the thing you probably care about. These are the seven LiveBench categories scored separately — Gemini 3.5 Flash on top, Gemini 3.7 Flash below, both out of 100.

Agentic coding
49.0
58.3
Codingtoo close to call
78.2
78.9
Reasoning
82.0
87.8
Mathematics
88.2
93.5
Data analysis
64.9
68.0
Languagetoo close to call
84.6
85.5
Instruction following
75.6
79.9

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 3.5 FlashGemini 3.7 Flash
Support chatbot

1.2K in / 400 out × 200K requests

$982.80/mo$215.70/mo
RAG assistant

8K in / 600 out × 100K requests

$1200.00/mo$277.50/mo
Coding agent

40K in / 4K out × 20K requests

$1164.00/mo$261.00/mo
Document extraction

20K in / 1.5K out × 50K requests

$2107.50/mo$498.75/mo
Bulk classification

500 in / 20 out × 5M requests

$3975.00/mo$956.25/mo
Run these two through the cost calculator

Which should you pick?

You are running this at volume

Gemini 3.7 Flash

Lowest measured cost per point of capability at $0.0875 per point — the gap compounds with every request.

Quality matters more than the bill

Gemini 3.7 Flash

Highest overall LiveBench score of the two at 78.8.

The workload is coding or agentic work

Gemini 3.7 Flash

Leads on agentic coding — 58.3 against 49.0.

Gemini 3.5 Flash vs Gemini 3.7 Flash FAQ

Which is better, Gemini 3.5 Flash or Gemini 3.7 Flash?

Gemini 3.7 Flash wins outright — it scores higher and costs less. Gemini 3.7 Flash leads by 4.2 points overall while listing 4.5× cheaper per blended million tokens. There is no trade-off to reason about here; the only reason to pick the other is a constraint this table does not show, like an existing contract, a region, or a provider you are already on. Gemini 3.7 Flash also leads on measured cost per point of capability, at $0.0875 per point.

Is Gemini 3.5 Flash cheaper than Gemini 3.7 Flash?

Gemini 3.7 Flash is cheaper. On a 3:1 input:output blend, Gemini 3.5 Flash lists at $3.38 per million tokens and Gemini 3.7 Flash at $0.750 — Gemini 3.7 Flash is 4.5× cheaper. Input and output are priced separately — Gemini 3.5 Flash charges $1.50 in and $9.00 out, Gemini 3.7 Flash charges $0.375 and $1.88 — so the model that looks cheaper flips depending on how output-heavy your workload is.

Gemini 3.5 Flash vs Gemini 3.7 Flash: which scores higher on benchmarks?

Gemini 3.5 Flash scores 74.6 and Gemini 3.7 Flash scores 78.8 overall on LiveBench, the mean of its seven categories. That is a 4.2-point lead for Gemini 3.7 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 3.5 Flash or Gemini 3.7 Flash?

Gemini 3.7 Flash. 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 3.5 Flash works out at $0.1357 per point and Gemini 3.7 Flash at $0.0875.

Does Gemini 3.5 Flash or Gemini 3.7 Flash have a bigger context window?

They are effectively the same — 1.0M for Gemini 3.5 Flash and 1.0M for Gemini 3.7 Flash.

Do Gemini 3.5 Flash and Gemini 3.7 Flash support prompt caching?

Both publish a cached-input rate: $0.150 per million for Gemini 3.5 Flash and $0.037 for Gemini 3.7 Flash, against full input rates of $1.50 and $0.375. On a workload with a long stable prefix — a system prompt, a tool schema, a retrieved corpus — that changes the economics more than the headline price does.

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.
  • ScoresLiveBench release 2026-06-25, 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.