Models / GPT-5.3-Codex
GPT-5.3-Codex
16th most intelligent of the 34 models priced $3–10 per million tokens. Among models available today, in the top quarter for reasoning and knowledge.
- Price per million tokens
- $1.75 in · $14.00 out
Typical host on OpenRouter, 7 Oct 2026 · compare prices - Speed
- 94 tokens a second
Artificial Analysis, on its usual API - Intelligence Index
- 32.5
Artificial Analysis - Spring Prompt overall
- Not ranked yet
needs our benchmarks and two groups of results - Developer
- OpenAI, based in the US
Weights not published - Released
- Not recorded
400,000 tokens of context
Where it stands among the models you could choose
Ranked only among models available today (those you can call through OpenRouter), in the groups people choose between: the same price band, similar intelligence, the same speed, developers based in the same place. Retired models are left out.
| Among | Intelligence Index | Spring Prompt overall | Price | Speed |
|---|---|---|---|---|
| Models available today | 42nd of 195Claude Opus 5.5 | – | 196th of 221Mistral Nemo | 36th of 63Trinity Large Thinking |
| Models priced $3–10 per million tokens | 16th of 34Claude Opus 5.5 | – | the group | 7th of 13Mistral Medium 3.5 |
| Models with similar intelligence | the group | – | 12th of 17DeepSeek V4 Flash 0423 | 4th of 6DeepSeek V4 Flash Vision Exp |
| Models writing 50–100 tokens a second | 9th of 23Claude Opus 5.5 | – | 20th of 23Granite 4.2 8B | the group |
| Models from developers based in the US | 28th of 93Claude Opus 5.5 | – | 79th of 103Llama 3.1 8B Instruct | 22nd of 32Trinity Large Thinking |
The name under each rank is the leader of that group. Price is the developer's list price (or the typical OpenRouter host where we haven't read one) for three input tokens to one output; intelligence and speed are from Artificial Analysis (data sourced from Artificial Analysis); "similar intelligence" means within 4 points on its Intelligence Index. Developer locations are where each company is based, not where a model is served. Groups under 3 models are not ranked.
How good is it, and for what?
Each use case ranks the available models its benchmarks measured, then those priced $3–10 per million tokens. The bar shows where it falls in that field, best to the right. Open a row for the results behind it.
Product listingsTurning a sparse product feed and photos into listings that can go live
- CatalogBench: has not measured this model.
Decks from an analysisTurning a finished analysis into a deck you could present as it is
- DeckBench: has not measured this model.
Marketing planningPlanning a year of ad spend without overspending
- ROASBench: has not measured this model.
Agents and tool useMulti-step tasks with tools: support desks, coding agents, function calls
| Benchmark | GPT-5.3-Codex | Rank among available | Best available |
|---|---|---|---|
| Vending-Bench 2Vending-Bench 2: money after a year · reported | $5,940.12 | 17th of 56 | GPT-6 Astra $15,514.70 |
- tau2-bench: has not measured this model.
- Berkeley Function Calling Leaderboard (BFCL) V4: has not measured this model.
- OpenHands Index: has not measured this model.
- Microsoft STATE-Bench: has not measured this model.
- Artificial Analysis: has not measured this model.
Professional workReal tasks from banking, consulting and law, business documents and freelance projects
- APEX-Agents: has not measured this model.
- GDP.pdf: has not measured this model.
- Remote Labor Index: has not measured this model.
Reasoning and knowledgeHard questions across science, maths and general knowledge
| Benchmark | GPT-5.3-Codex | Rank among available | Best available |
|---|---|---|---|
| Artificial AnalysisArtificial Analysis: Artificial Analysis Intelligence Index · reported | 32.5 | 42nd of 195 | Claude Opus 5.5 57.6 |
| Artificial AnalysisArtificial Analysis: Humanity's Last Exam · reported | 42.5% | 27th of 194 | Claude Opus 5.5 61.4% |
| Artificial AnalysisArtificial Analysis: GPQA Diamond · reported | 91.5% | 25th of 185 | GPT-6 Astra 96.3% |
WritingWhat people prefer in blind comparisons, and judged writing quality
- Arena (formerly LMArena): has not measured this model.
- UGI Leaderboard: has not measured this model.
Sticking to the factsSummarising without inventing things, and factual answers
- Vectara Hallucination Leaderboard: has not measured this model.
- Arena (formerly LMArena): has not measured this model.
- SimpleQA Verified (Epoch AI): has not measured this model.
Speed under pressureGood decisions against a real clock (fast chess)
- BulletBench: has not measured this model.
Against the alternatives
The models you would most likely weigh it against: the leaders of the groups above.
| GPT-5.3-Codex | Claude Opus 5.5 most intelligent at $3–10 per million tokens | DeepSeek V4 Flash 0423 cheapest with similar intelligence | DeepSeek V4 Flash Vision Exp fastest with similar intelligence | |
|---|---|---|---|---|
| Price per million tokens | $4.81 | $8.00 | $0.18 | $0.66 |
| Tokens a second | 94 | 97 | – | 217 |
| Intelligence Index | 32.5 | 57.6 | 34.3 | 34.8 |
| Spring Prompt overall | – | 82 | – | – |
| Rank among available models, by use case | ||||
| Product listings | – | 6th | – | – |
| Decks from an analysis | – | 5th | – | – |
| Marketing planning | – | 5th | – | – |
| Agents and tool use | 44th of 161 | 2nd | 34th | 35th |
| Professional work | – | 3rd | – | – |
| Reasoning and knowledge | 33rd of 195 | 1st | 40th | 41st |
| Writing | – | 4th | 62nd | – |
| Sticking to the facts | – | 6th | 81st | – |
| Speed under pressure | – | – | – | – |
Shaded figures are better than GPT-5.3-Codex's. A dash means no result.
Where it has been measured
- Artificial Analysis9 results
- Vending-Bench 21 result
- BulletBenchNot measured
- CatalogBenchNot measured
- DeckBenchNot measured
- ROASBenchNot measured
- APEX-AgentsNot measured
- Arena (formerly LMArena)Not measured
- Berkeley Function Calling Leaderboard (BFCL) V4Not measured
- GDP.pdfNot measured
- Microsoft STATE-BenchNot measured
- OpenHands IndexNot measured
- Remote Labor IndexNot measured
- SimpleQA Verified (Epoch AI)Not measured
- tau2-benchNot measured
- UGI LeaderboardNot measured
- Vectara Hallucination LeaderboardNot measured
Every result
10 published results from 2 sources, each in the source's own units, with the configuration that produced it.
Artificial Analysis · reported by Artificial Analysis · 9 results
| Measure | Value | Rank | Configuration | Dated |
|---|---|---|---|---|
| Artificial Analysis: Artificial Analysis Intelligence Indexindex score, higher is better | 32.5 | 97 of 402 | GPT-5.3-Codex (xhigh) | 7 Oct 2026 |
| Artificial Analysis: GPQA Diamond% of questions, higher is better | 91.5% | 47 of 359 | GPT-5.3-Codex (xhigh) | 7 Oct 2026 |
| Artificial Analysis: Humanity's Last Exam% of questions, higher is better | 42.5% | 58 of 400 | GPT-5.3-Codex (xhigh) | 7 Oct 2026 |
| Artificial Analysis: IFBench% of instructions, higher is better | 75.4% | 30 of 288 | GPT-5.3-Codex (xhigh) | 7 Oct 2026 |
| Artificial Analysis: Long-context reasoning (AA-LCR)% of questions, higher is better | 83.3% | 18 of 388 | GPT-5.3-Codex (xhigh) | 7 Oct 2026 |
| Artificial Analysis: Output speedtokens per second, higher is better | 94.1 | 63 of 128 | GPT-5.3-Codex (xhigh) | 7 Oct 2026 |
| Artificial Analysis: Terminal-Bench Hard% of tasks, higher is better | 53.0% | 17 of 282 | GPT-5.3-Codex (xhigh) | 7 Oct 2026 |
| Artificial Analysis: Time to first answer tokenseconds, lower is better | 34.5 | 97 of 128 | GPT-5.3-Codex (xhigh) | 7 Oct 2026 |
| Artificial Analysis: τ²-bench telecom% of tasks, higher is better | 86.0% | 76 of 286 | GPT-5.3-Codex (xhigh) | 7 Oct 2026 |
Not shown: Not business work, and a blend: read the parts for any one task.
Vending-Bench 2 · reported by Vending-Bench 2 · 1 result
| Measure | Value | Rank | Configuration | Dated |
|---|---|---|---|---|
| Vending-Bench 2: money after a yearUS dollars, higher is better | $5,940.12 | 17 of 63 | GPT-5.3-Codex | 1 Oct 2026 |
Not shown: Not a real business; one simulated market with set rules.