Model comparison
GPT-5.2-Codex vs o1
Head-to-head evidence from 11 shared benchmark results across 5 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: GPT-5.2-Codex #64 (Supported); o1 #137 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GPT-5.2-Codex and o1 share 11 comparable benchmark results. 0 of 8 categories are comparable. 4 results are unique to GPT-5.2-Codex; 5 to o1.
Updated July 24, 2026- Shared results
- 11
- GPT-5.2-Codex only
- 4
- o1 only
- 5
- Comparable categories
- 0 / 8
Benchmark data for GPT-5.2-Codex and o1 is coming soon on BenchLM.
Confidence note. This is a partial-evidence comparison with 11 shared benchmark results across 5 evidence categories; 0 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
BenchLM has partial data for these models, but not enough overlapping benchmark coverage to produce a fair score-level comparison yet.
o1 is priced at $15.00 input / $60.00 output per 1M tokens, versus $1.75 input / $14.00 output per 1M tokens for GPT-5.2-Codex. GPT-5.2-Codex has the larger context window at 400K, compared with 200K for o1.
Category breakdown
Exact category averages are shown below. Not measured means BenchLM does not have enough sourced public coverage for that model and category.
| Category | GPT-5.2-Codex | Δ | o1 |
|---|---|---|---|
| Knowledge | GPT-5.2-CodexNot measured | MarginNo overlap | o175.7 |
| Math | GPT-5.2-CodexNot measured | MarginNo overlap | o19.3 |
| Inst. Following | GPT-5.2-CodexNot measured | MarginNo overlap | o192.2 |
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | GPT-5.2-Codex | o1 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GPT-5.2-Codex$1.75 input / $14 output | o1$15 input / $60 output | GPT-5.2-Codex has the lower combined listed price. |
| Generation speedtokens per second | GPT-5.2-Codex123 tok/s | o198 tok/s | GPT-5.2-Codex has the higher measured throughput. |
| First-answer latencyseconds to first token | GPT-5.2-Codex87.34 s | o132.29 s | o1 reaches the first token sooner. |
| Context windowmaximum listed tokens | GPT-5.2-Codex400K | o1200K | GPT-5.2-Codex lists the larger context window. |
Benchmark Deep Dive
Agentic3 benchmarks
Coding3 benchmarks
Reasoning2 benchmarks
Knowledge8 benchmarks
| Benchmark | GPT-5.2-Codex | o1 | Result |
|---|---|---|---|
| Artificial Analysis Intelligence IndexSource | 40.1% | 23.4% | GPT-5.2-Codex leads |
| AA-GPQA DiamondSource | 89.9% | 74.7% | GPT-5.2-Codex leads |
| AA-HLESource | 33.5% | 7.7% | GPT-5.2-Codex leads |
| AA-Omniscience IndexSource | -2.5% | -10.5% | GPT-5.2-Codex leads |
| AA-Omniscience AccuracySource | 40.7% | 34.7% | GPT-5.2-Codex leads |
| AA-Omniscience Hallucination RateSource | 72.8% | 69.3% | o1 leads |
| MMLUSource | — | 91.8% | Not comparable |
| GPQASource | — | 75.7% | Not comparable |
Math1 benchmarks
| Benchmark | GPT-5.2-Codex | o1 | Result |
|---|---|---|---|
| FrontierMath v2 (Tiers 1-3)Source | — | 9.310% | Not comparable |
Multimodal1 benchmarks
| Benchmark | GPT-5.2-Codex | o1 | Result |
|---|---|---|---|
| AA-MMMU-ProSource | 76.3% | — | Not comparable |
Frequently Asked Questions (3)
Can I compare GPT-5.2-Codex and o1 on BenchLM yet?
Not fully yet. BenchLM is tracking both models, but the sourced benchmark breakdown for this comparison is still coming soon.
Why does this comparison show “coming soon”?
BenchLM only shows category winners and benchmark-level calls when we have sourced results that can be compared fairly. For these models, the public benchmark coverage is not complete enough yet.
What data is available for GPT-5.2-Codex and o1 today?
GPT-5.2-Codex: $1.75 input / $14.00 output per 1M tokens o1: $15.00 input / $60.00 output per 1M tokens Both model pages still include creator, context window, reasoning mode, and other metadata while benchmark coverage fills in.