Gemini 3.6 Flash vs GPT-5.2-Codex: which should you choose in 2026?
Gemini 3.6 Flash (by Google) and GPT-5.2-Codex (by OpenAI) are compared below. Here is how they stack up on benchmarks, price, and capabilities, and which one to pick in 2026.
No protocol-matched benchmark supports a controlled winner. Looking only at published point scores, Gemini 3.6 Flash is higher on 0 benchmarks, while GPT-5.2-Codex is higher on 1 benchmark (LiveBench).
The evaluation setups are not fully aligned, so these are not controlled head-to-head wins.
Gemini 3.6 Flash is about 1.6× cheaper on a blended 3:1 input/output basis ($3.00 vs $4.81 per 1M tokens).
Gemini 3.6 Flash has the larger context window (1,048,576 tokens vs 400,000 tokens).
Choose Gemini 3.6 Flash if…
- • lower blended API cost matters for your workload
- • you work with longer documents, transcripts, or codebases
Choose GPT-5.2-Codex if…
- • your own prompt tests favor its output; shared comparable evidence does not identify a unique advantage
The benchmark count includes only results measured with a matching benchmark version and protocol. Arena scores are shown separately. Missing, preliminary, and incompatible data is not treated as a controlled win. Published point-score comparisons are labeled separately when protocol details are incomplete. For text-output models, the verdict also compares token pricing and context windows.
Performance benchmarks
Every value links to its source. A dash means that no reviewed result is available for that exact model and protocol.
| Benchmark | Gemini 3.6 Flash | GPT-5.2-Codex |
|---|---|---|
A 657-task benchmark of multi-step work across simulated SaaS applications in six business domains. The Artificial Analysis protocol reports a guardrail-aware score and is distinct from both the public Zapier split and the unrelated dynamic AutoBench framework. Guardrail-aware score | 51.1% Artificial Analysis | — |
A benchmark of difficult, verifiable information-seeking questions designed to measure an agent's ability to locate hard-to-find facts through web browsing. Accuracy | — | — |
A verified computer-use benchmark in which multimodal agents operate desktop applications and are graded from the resulting environment state. Mean task reward | — | — |
A long-horizon software-engineering benchmark with 113 original tasks graded by hand-written tests. Pass@1 | 49.0%±5.0 DataCurve | — |
A human-validated subset of real GitHub issues used to measure whether a coding agent can produce repository patches that resolve the associated tests. Resolved | — | 72.8% SWE-bench |
Version 2.1 of the benchmark for completing realistic tasks in terminal environments. Harness, resource limits, and attempt count are part of the protocol. Mean task success | — | — |
The highest-quality subset of Graduate-Level Google-Proof Q&A, designed to test expert-level scientific reasoning in biology, physics, and chemistry. Accuracy | 92.8% Artificial Analysis | — |
A 2,500-question expert-level benchmark spanning dozens of academic fields. Tool-assisted and no-tools results are separate protocols and must not be merged. Accuracy | 40.8% Artificial Analysis | — |
A contamination-resistant benchmark refreshed on a fixed release cadence. Scores from different LiveBench releases must never be compared as the same protocol. Mean of category averages Protocols differ or are incompletely disclosed; not counted as a controlled win | 73.6% LiveBench | 74.0% LiveBench |
| Arena preference scores | ||
Human preference score | 1485 ±6Preliminary | — |
Human preference score for code and web development | 1536 ±11Preliminary | 1338 ±9 |
Benchmark sources
- Arena AI leaderboard
- AutomationBench-AA: Agentic SaaS Workflow Benchmark
- DeepSWE 1.1 Leaderboard
- SWE-bench Verified Bash Only Leaderboard
- GPQA Diamond Benchmark Leaderboard
- Humanity's Last Exam Benchmark Leaderboard
- LiveBench 2026-06-25 Leaderboard
Reviewed evidence last updated 2026-08-09. Arena data last refreshed Aug 9, 2026.
Pricing, capabilities, and model facts
| Feature | Gemini 3.6 Flash | GPT-5.2-Codex |
|---|---|---|
| Context & model facts | ||
| Developer | OpenAI | |
| API provider | OpenAI | |
| Input context | 1,048,576 tokens | 400,000 tokens |
| Maximum output | 65,536 tokens | 128,000 tokens |
| Released | — | — |
| Added to Writingmate | Jul 21, 2026 | Jan 14, 2026 |
| License | Proprietary | Proprietary |
| Knowledge cutoff | — | — |
| Capabilities | ||
| Inputs | Text, Image, Video, File, Audio | Text, Image |
| Outputs | Text | Text |
| Tool use | Yes | Yes |
| Reasoning | Yes | Yes |
| Vision | Yes | Yes |
| Image Generation | No | No |
| Video Generation | No | No |
| API pricing | ||
| Input (per 1M tokens) | $1.50 | $1.75 |
| Output (per 1M tokens) | $7.50 | $14.00 |
| Blended 3:1 input/output | $3.00 | $4.81 |
| API performance | ||
| p95 latency | Not measured | Not measured |
| Output throughput | Not measured | Not measured |
| Writingmate shows API performance only when both models have enough observations from the same measurement window, prompt profile, and provider. Third-party latency values are not copied into this table. | ||
Data sources
- Writingmate model catalog (pricing, limits, and availability)
Catalog data last updated Jul 21, 2026.
Why Pay for Multiple Subscriptions?
Comparing Gemini 3.6 Flash from Google with GPT-5.2-Codex from OpenAI? Instead of managing separate API keys and subscriptions, get both with Writingmate.
| Plan | Price | Gemini 3.6 Flash | GPT-5.2-Codex | AI Images | AI Video |
|---|---|---|---|---|---|
Writingmate Pro Most popular | $20/mo | Included | Included | Nano Banana Pro, FLUX.2, DALL-E & more | Sora 2, VEO 3.1 |
Writingmate Ultimate Power users | $60/mo | Included | Included | Nano Banana Pro, FLUX.2, DALL-E & more | Sora 2, VEO 3.1 |
Gemini 3.6 Flash vs GPT-5.2-Codex FAQ
Which is better, Gemini 3.6 Flash or GPT-5.2-Codex?
No protocol-matched benchmark supports a controlled winner. Looking only at published point scores, Gemini 3.6 Flash is higher on 0 benchmarks, while GPT-5.2-Codex is higher on 1 benchmark (LiveBench). The evaluation setups are not fully aligned, so these are not controlled head-to-head wins. Gemini 3.6 Flash is about 1.6× cheaper on a blended 3:1 input/output basis ($3.00 vs $4.81 per 1M tokens). Gemini 3.6 Flash has the larger context window (1,048,576 tokens vs 400,000 tokens).
Which model is cheaper to use through an API?
Gemini 3.6 Flash is about 1.6× cheaper on a blended 3:1 input/output basis ($3.00 vs $4.81 per 1M tokens).
Which model supports more context?
Gemini 3.6 Flash has the larger context window (1,048,576 tokens vs 400,000 tokens).
Can I switch between Gemini 3.6 Flash and GPT-5.2-Codex?
Yes. Use the model selector on this page to open any current Writingmate model comparison. You can also run the same prompt with both models in Writingmate.