Model comparison
Gemini 3.1 Pro vs GPT-5.3-Codex
Google against OpenAI, compared on context, price, and verified benchmark results.
Catalog record checked September 5, 2026Individual provider fields may changeEvidence confidence: High — see receipts below
Gemini 3.1 Pro
Frontier
OpenAI
GPT-5.3-Codex
Frontier
| Specification | Gemini 3.1 Pro | GPT-5.3-Codex |
|---|---|---|
| Provider | OpenAI | |
| Tier | Frontier | Frontier |
| Context window | Winner: 1.05M | 400K |
| Max output | 66K | Winner: 128K |
| Input / 1M tokens | $2 | Winner: $1.75 |
| Output / 1M tokens | Winner: $12 | $14 |
| Weights | Closed | Closed |
| Parameters | Not disclosedUnverified | Not disclosedUnverified |
| Reasoning levels | low, medium, high | none, low, medium, high, xhigh, max |
| Modalities | text, image, video, audio, pdf | text, image |
| API model id | gemini-3.1-pro-preview | gpt-5.3-codex |
| Released | February 19, 2026 | February 5, 2026 |
| Artificial Analysis Intelligence Index (2026-08-14) | 60 | Not verifiedUnverified |
| Artificial Analysis Intelligence Index [high] (2026-08-14) | Not verifiedUnverified | 52 |
| SWE-bench Verified (2026-02) | 80.6 | Not verifiedUnverified |
| GPQA Diamond (2026-02) | 94.3 | Not verifiedUnverified |
| Humanity's Last Exam (2026-02) | 44.4 | Not verifiedUnverified |
| Terminal-Bench 2.1 (2026-07) | 73.8 | Not verifiedUnverified |
Prices are USD per million tokens at standard rates, excluding batch and caching discounts. Bold indicates the better figure where one is objectively better. Values we could not confirm from the provider are shown as “Not verified” rather than estimated.
Benchmark receipts
- 2026-08-14: Artificial AnalysisComposite index blending reasoning, knowledge, and coding evals into one 0–100 score. Comparability: directly comparable — AA occasionally rebaselines the index scale between snapshots — a score captured on one date is only comparable to same-snapshot scores (check measuredAt).
- 2026-07: Google DeepMind model evaluation report (vendor, Terminus-2; own Feb report separately reported TB 2.0 = 68.5 — versions preserved)Agentic terminal work: multi-step tasks executed in a sandboxed shell environment. Comparability: comparable with caveat — Scores across Terminal-Bench major versions (v1 vs v2) are NOT comparable; harness configuration (container, time limits) also shifts results.
- 2026-02: Google DeepMind model evaluation report (vendor, no tools; 51.4 with search+code)Real GitHub issue resolution: does the model's patch pass the hidden tests. Comparability: comparable with caveat — Post-audit vendor claims and pre-audit scores sit on different task trust levels; scaffolding (agent harness, compute budget) also dominates results. Never aggregate across scaffolds.
Pricing tiers: Gemini 3.1 Pro: $2/$12 per MTok (≤200K); $4/$18 (>200K). Still 'Preview' as of 2026-08-29; 3.5 Pro not yet released. · GPT-5.3-Codex: Standard $1.75/$14 per MTok; cached input $0.175/MTok; Fast mode $3.50/$28. Purpose-built for the Codex agent.
Gemini 3.1 Pro
Gemini 3.1 Pro is Google's current flagship Pro model with record benchmark scores and a 1M-token context.
Best for
- Very long documents
- Multimodal reasoning
- Frontier knowledge work
Watch out
Still in Preview; 3.5 Pro is 'coming soon' but unreleased as of 2026-08-29.
GPT-5.3-Codex
GPT-5.3-Codex is OpenAI's coding-specialised model powering the Codex agent at $1.75/$14.
Best for
- Autonomous coding
- Repo-scale refactors
- Test generation
Watch out
Tuned for coding, not general chat; use GPT-5.6 for broad reasoning.
When the cheaper one wins
Gemini 3.1 Pro is cheaper on output at $12 per million tokens against $14 for GPT-5.3-Codex — about 1.2×. Use the cheaper tier for classification, extraction, summarisation, and any task where the expensive model’s extra score does not change the accepted output. The expensive one only pays if your hardest task actually fails on the cheap tier. These are standard-tier API rates, excluding batch and cache discounts.
Run the model pickerEvidence confidence: High
How strong and complete the evidence behind this comparison is — not a prediction of which model is better.
- Pricing verified on both sides — Input and output rates are verified for both models.
- 4/5 core specs verified on both sides — Not published for at least one side: parameter count.
- 1 shared named benchmark with differing scores — Measured on: Artificial Analysis Intelligence Index.
- Verified within the last 90 days — Newest catalog check was 6 days ago.
- Both models carry source citations — Each side has at least two catalog sources on record.
Source receipts
Each catalog figure was checked against the provider or an independent second source on the date shown.
- Gemini 3.1 Pro: Google Cloud — Gemini 3.1 Pro (accessed 2026-08-29)
- Gemini 3.1 Pro: TechCrunch — Gemini 3.1 Pro (accessed 2026-08-29)
- GPT-5.3-Codex: OpenAI — Introducing GPT-5.3-Codex (accessed 2026-08-29)
- GPT-5.3-Codex: OpenAI API pricing (gpt-5.3-codex $1.75/$14) (accessed 2026-08-29)
Related comparisons
- Claude Fable 5.1 vs Gemini 3.1 Pro
- Claude Fable 5.1 vs GPT-5.3-Codex
- Claude Fable 5 vs Gemini 3.1 Pro
- Claude Fable 5 vs GPT-5.3-Codex
- Claude Mythos 5.1 vs Gemini 3.1 Pro
- Claude Mythos 5.1 vs GPT-5.3-Codex
Diving deeper on one model? Gemini 3.1 Pro · GPT-5.3-Codex
Common questions
Gemini 3.1 Pro vs GPT-5.3-Codex
Answered from the verified figures on this page rather than general guidance.
Is Gemini 3.1 Pro or GPT-5.3-Codex cheaper for input?
GPT-5.3-Codex is cheaper at $1.75 per million input tokens, against $2 for Gemini 3.1 Pro — roughly 1.1× the price. Output tokens usually dominate a real bill, so weigh the output rate more heavily than the input rate. These are base rates; Gemini 3.1 Pro has tiered pricing: $2/$12 per MTok (≤200K); $4/$18 (>200K). Still 'Preview' as of 2026-08-29; 3.5 Pro not yet released. GPT-5.3-Codex has tiered pricing: Standard $1.75/$14 per MTok; cached input $0.175/MTok; Fast mode $3.50/$28. Purpose-built for the Codex agent.
Is Gemini 3.1 Pro or GPT-5.3-Codex cheaper for output?
Gemini 3.1 Pro is cheaper at $12 per million output tokens, against $14 for GPT-5.3-Codex — roughly 1.2× the price. Output tokens usually dominate a real bill, so weigh the output rate more heavily than the input rate. These are base rates; Gemini 3.1 Pro has tiered pricing: $2/$12 per MTok (≤200K); $4/$18 (>200K). Still 'Preview' as of 2026-08-29; 3.5 Pro not yet released. GPT-5.3-Codex has tiered pricing: Standard $1.75/$14 per MTok; cached input $0.175/MTok; Fast mode $3.50/$28. Purpose-built for the Codex agent.
Which has the larger context window, Gemini 3.1 Pro or GPT-5.3-Codex?
Gemini 3.1 Pro accepts 1.05M tokens against 400K for GPT-5.3-Codex. This only matters if you routinely send very long documents or large codebases.
Do Gemini 3.1 Pro and GPT-5.3-Codex support the same reasoning levels?
Gemini 3.1 Pro exposes low, medium, high, while GPT-5.3-Codex exposes none, low, medium, high, xhigh, max.
Should I use Gemini 3.1 Pro or GPT-5.3-Codex?
Both sit in the frontier tier, so the choice usually comes down to price and context rather than capability. Gemini 3.1 Pro suits very long documents; GPT-5.3-Codex suits autonomous coding.
Next step
Choosing between them
The verified figures that separate this pair, computed from the catalog rather than restated boilerplate.
- Input price: GPT-5.3-Codex costs $1.75 per 1M tokens versus $2 for Gemini 3.1 Pro — a 1.1x difference at the headline tier.
- Context: Gemini 3.1 Pro takes 1.05M against 400K for GPT-5.3-Codex — only decisive if your prompts approach the smaller window.
- Measured capability: Gemini 3.1 Pro leads Artificial Analysis Intelligence Index 60 to 52 (measured 2026-08-14).
Choosing a harness rather than a model — Cursor, Copilot, Claude Code, Windsurf, or Lovable? Compare agentic harnesses · Latest releases.