// head_to_head
Gemini 3.5 Flash vs GPT-5.2-Codex
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.
Effectively the same quality — Gemini 3.5 Flash is the cheaper way to get it.
The two are within 0.7 points overall, which is inside the range that effort settings alone move a LiveBench score, so treat them as quality-equivalent. Gemini 3.5 Flash lists 43% cheaper per blended million tokens. When quality ties, cost is the whole decision. The two cost measures disagree here, which is worth knowing: Gemini 3.5 Flash has the lower sticker price, but GPT-5.2-Codex earns each point of capability for less — $0.1001 against $0.1357 — because per-token rates do not predict how many tokens a model actually spends on a task.
Gemini 3.5 Flash
- Blended / 1M
- $3.38
- Context
- 1.0M
- Released
- May 19, 2026
- Overall score
- 74.6
openai
GPT-5.2-Codex
- Blended / 1M
- $4.81
- Context
- 400K
- Released
- Jan 14, 2026
- Overall score
- 74.0
Specs and pricing
| Metric | Gemini 3.5 Flash | GPT-5.2-Codex |
|---|---|---|
| LiveBench overall Mean of the seven LiveBench category scores, 0–100. Higher is better. | 74.6 | 74.0 |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | $0.1357 | $0.1001win |
| Blended price / 1M 3:1 input:output mix, the usual shape of production traffic. | $3.38win | $4.81 |
| Input price / 1M | $1.50win | $1.75 |
| Output price / 1M | $9.00win | $14.00 |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | $0.150win | $0.175 |
| Context window | 1.0Mwin | 400K |
| Max output tokens | 66K | 128Kwin |
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, GPT-5.2-Codex 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 | Gemini 3.5 Flash | GPT-5.2-Codex |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $982.80/mo | $1426.60/mo |
| RAG assistant 8K in / 600 out × 100K requests | $1200.00/mo | $1610.00/mo |
| Coding agent 40K in / 4K out × 20K requests | $1164.00/mo | $1638.00/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $2107.50/mo | $2721.25/mo |
| Bulk classification 500 in / 20 out × 5M requests | $3975.00/mo | $4987.50/mo |
Which should you pick?
You are running this at volume
GPT-5.2-Codex
Lowest measured cost per point of capability at $0.1001 per point — the gap compounds with every request.
You need to fit large documents in one call
Gemini 3.5 Flash
Wider context window — 1.0M against 400K.
Gemini 3.5 Flash vs GPT-5.2-Codex FAQ
Which is better, Gemini 3.5 Flash or GPT-5.2-Codex?
Effectively the same quality — Gemini 3.5 Flash is the cheaper way to get it. The two are within 0.7 points overall, which is inside the range that effort settings alone move a LiveBench score, so treat them as quality-equivalent. Gemini 3.5 Flash lists 43% cheaper per blended million tokens. When quality ties, cost is the whole decision. The two cost measures disagree here, which is worth knowing: Gemini 3.5 Flash has the lower sticker price, but GPT-5.2-Codex earns each point of capability for less — $0.1001 against $0.1357 — because per-token rates do not predict how many tokens a model actually spends on a task.
Is Gemini 3.5 Flash cheaper than GPT-5.2-Codex?
Gemini 3.5 Flash is cheaper. On a 3:1 input:output blend, Gemini 3.5 Flash lists at $3.38 per million tokens and GPT-5.2-Codex at $4.81 — Gemini 3.5 Flash is 43% cheaper. Input and output are priced separately — Gemini 3.5 Flash charges $1.50 in and $9.00 out, GPT-5.2-Codex charges $1.75 and $14.00 — so the model that looks cheaper flips depending on how output-heavy your workload is.
Gemini 3.5 Flash vs GPT-5.2-Codex: which scores higher on benchmarks?
Gemini 3.5 Flash scores 74.6 and GPT-5.2-Codex scores 74.0 overall on LiveBench, the mean of its seven categories. That gap is inside the range that effort settings alone move a score, so treat them as equivalent on published quality. 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 GPT-5.2-Codex?
GPT-5.2-Codex. 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 GPT-5.2-Codex at $0.1001.
Does Gemini 3.5 Flash or GPT-5.2-Codex have a bigger context window?
Gemini 3.5 Flash has the larger context window: 1.0M for Gemini 3.5 Flash against 400K for GPT-5.2-Codex. 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 3.5 Flash and GPT-5.2-Codex support prompt caching?
Both publish a cached-input rate: $0.150 per million for Gemini 3.5 Flash and $0.175 for GPT-5.2-Codex, against full input rates of $1.50 and $1.75. 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.
- Scores — LiveBench 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.