// head_to_head
DeepSeek V4.1 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.
DeepSeek V4.1 Flash wins outright — it scores higher and costs less.
DeepSeek V4.1 Flash leads by 7.1 points overall while listing 18× 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. DeepSeek V4.1 Flash also leads on measured cost per point of capability, at $0.0157 per point.
deepseek
DeepSeek V4.1 Flash
- Blended / 1M
- $0.262
- Context
- 1.0M
- Released
- Sep 10, 2026
- Overall score
- 81.1
openai
GPT-5.2-Codex
- Blended / 1M
- $4.81
- Context
- 400K
- Released
- Jan 14, 2026
- Overall score
- 74.0
Specs and pricing
| Metric | DeepSeek V4.1 Flash | GPT-5.2-Codex |
|---|---|---|
| LiveBench overall Mean of the seven LiveBench category scores, 0–100. Higher is better. | 81.1win | 74.0 |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | $0.0157win | $0.1001 |
| Blended price / 1M 3:1 input:output mix, the usual shape of production traffic. | $0.262win | $4.81 |
| Input price / 1M | $0.150win | $1.75 |
| Output price / 1M | $0.600win | $14.00 |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | $0.0030win | $0.175 |
| Context window | 1.0Mwin | 400K |
| Max output tokens | 384Kwin | 128K |
Benchmarks by category
An overall score averages away the thing you probably care about. These are the seven LiveBench categories scored separately — DeepSeek V4.1 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 | DeepSeek V4.1 Flash | GPT-5.2-Codex |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $73.42/mo | $1426.60/mo |
| RAG assistant 8K in / 600 out × 100K requests | $97.20/mo | $1610.00/mo |
| Coding agent 40K in / 4K out × 20K requests | $85.68/mo | $1638.00/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $187.65/mo | $2721.25/mo |
| Bulk classification 500 in / 20 out × 5M requests | $361.50/mo | $4987.50/mo |
Which should you pick?
You are running this at volume
DeepSeek V4.1 Flash
Lowest measured cost per point of capability at $0.0157 per point — the gap compounds with every request.
Quality matters more than the bill
DeepSeek V4.1 Flash
Highest overall LiveBench score of the two at 81.1.
The workload is coding or agentic work
DeepSeek V4.1 Flash
Leads on agentic coding — 77.3 against 49.4.
You need to fit large documents in one call
DeepSeek V4.1 Flash
Wider context window — 1.0M against 400K.
DeepSeek V4.1 Flash vs GPT-5.2-Codex FAQ
Which is better, DeepSeek V4.1 Flash or GPT-5.2-Codex?
DeepSeek V4.1 Flash wins outright — it scores higher and costs less. DeepSeek V4.1 Flash leads by 7.1 points overall while listing 18× 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. DeepSeek V4.1 Flash also leads on measured cost per point of capability, at $0.0157 per point.
Is DeepSeek V4.1 Flash cheaper than GPT-5.2-Codex?
DeepSeek V4.1 Flash is cheaper. On a 3:1 input:output blend, DeepSeek V4.1 Flash lists at $0.262 per million tokens and GPT-5.2-Codex at $4.81 — DeepSeek V4.1 Flash is 18× cheaper. Input and output are priced separately — DeepSeek V4.1 Flash charges $0.150 in and $0.600 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.
DeepSeek V4.1 Flash vs GPT-5.2-Codex: which scores higher on benchmarks?
DeepSeek V4.1 Flash scores 81.1 and GPT-5.2-Codex scores 74.0 overall on LiveBench, the mean of its seven categories. That is a 7.1-point lead for DeepSeek V4.1 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, DeepSeek V4.1 Flash or GPT-5.2-Codex?
DeepSeek V4.1 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. DeepSeek V4.1 Flash works out at $0.0157 per point and GPT-5.2-Codex at $0.1001.
Does DeepSeek V4.1 Flash or GPT-5.2-Codex have a bigger context window?
DeepSeek V4.1 Flash has the larger context window: 1.0M for DeepSeek V4.1 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 DeepSeek V4.1 Flash and GPT-5.2-Codex support prompt caching?
Both publish a cached-input rate: $0.0030 per million for DeepSeek V4.1 Flash and $0.175 for GPT-5.2-Codex, against full input rates of $0.150 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.