Model comparison
Claude Sonnet 4.6 vs GPT-5.2-Codex
Head-to-head evidence from 15 shared benchmark results across 6 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: Claude Sonnet 4.6 #32 (Supported); GPT-5.2-Codex #58 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. Claude Sonnet 4.6 and GPT-5.2-Codex share 15 comparable benchmark results. 0 of 8 categories are comparable. 18 results are unique to Claude Sonnet 4.6; 0 to GPT-5.2-Codex.
Updated July 20, 2026- Shared results
- 15
- Claude Sonnet 4.6 only
- 18
- GPT-5.2-Codex only
- 0
- Comparable categories
- 0 / 8
Benchmark data for Claude Sonnet 4.6 and GPT-5.2-Codex is coming soon on BenchLM.
Confidence note. This is a partial-evidence comparison with 15 shared benchmark results across 6 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.
Claude Sonnet 4.6 is priced at $3.00 input / $15.00 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 Claude Sonnet 4.6.
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 | Claude Sonnet 4.6 | Δ | GPT-5.2-Codex |
|---|---|---|---|
| Agentic | Claude Sonnet 4.665.2 | MarginNo overlap | GPT-5.2-CodexNot measured |
| Coding | Claude Sonnet 4.669.1 | MarginNo overlap | GPT-5.2-CodexNot measured |
| Knowledge | Claude Sonnet 4.666.0 | MarginNo overlap | GPT-5.2-CodexNot measured |
| Math | Claude Sonnet 4.626.4 | MarginNo overlap | GPT-5.2-CodexNot measured |
| Multimodal | Claude Sonnet 4.677.4 | MarginNo overlap | GPT-5.2-CodexNot measured |
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | Claude Sonnet 4.6 | GPT-5.2-Codex | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | Claude Sonnet 4.6$3 input / $15 output | GPT-5.2-Codex$1.75 input / $14 output | GPT-5.2-Codex has the lower combined listed price. |
| Generation speedtokens per second | Claude Sonnet 4.644 tok/s | GPT-5.2-Codex123 tok/s | GPT-5.2-Codex has the higher measured throughput. |
| First-answer latencyseconds to first token | Claude Sonnet 4.61.48 s | GPT-5.2-Codex87.34 s | Claude Sonnet 4.6 reaches the first token sooner. |
| Context windowmaximum listed tokens | Claude Sonnet 4.6200K | GPT-5.2-Codex400K | GPT-5.2-Codex lists the larger context window. |
Benchmark Deep Dive
Agentic8 benchmarks
| Benchmark | Claude Sonnet 4.6 | GPT-5.2-Codex | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 59.1% | — | Not comparable |
| OSWorld-VerifiedSource | 72.1% | — | Not comparable |
| Claw-EvalSource | 67.8% | — | Not comparable |
| CyberGymSource | 65.2% | — | Not comparable |
| τ²-bench resultsSource | 79.5% | 92.1% | GPT-5.2-Codex leads |
| Gert LabsSource | 62.92% | 51.79% | Claude Sonnet 4.6 leads |
| OSWorld 2.0Source | 8.3% | — | Not comparable |
| JobBenchSource | 36.9% | 26.0% | Claude Sonnet 4.6 leads |
Coding7 benchmarks
| Benchmark | Claude Sonnet 4.6 | GPT-5.2-Codex | Result |
|---|---|---|---|
| SWE-bench VerifiedSource | 79.6% | — | Not comparable |
| SWE-RebenchSource | 60.7% | — | Not comparable |
| React Native EvalsSource | 80.6% | — | Not comparable |
| Vibe Code BenchSource | 51.48% | 37.91% | Claude Sonnet 4.6 leads |
| cursorBench31Source | 48.8% | — | Not comparable |
| AA-SciCodeSource | 46.9% | 54.6% | GPT-5.2-Codex leads |
| FrontierCode 1.1 MainSource | 24.3% | — | Not comparable |
Reasoning2 benchmarks
Knowledge10 benchmarks
| Benchmark | Claude Sonnet 4.6 | GPT-5.2-Codex | Result |
|---|---|---|---|
| GPQASource | 89.9% | — | Not comparable |
| SuperGPQASource | 95% | — | Not comparable |
| MMLU-ProSource | 79.2% | — | Not comparable |
| HLESource | 49% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 35.9% | 40.1% | GPT-5.2-Codex leads |
| AA-GPQA DiamondSource | 79.9% | 89.9% | GPT-5.2-Codex leads |
| AA-HLESource | 13.2% | 33.5% | GPT-5.2-Codex leads |
| AA-Omniscience IndexSource | -2.9% | -2.5% | GPT-5.2-Codex leads |
| AA-Omniscience AccuracySource | 38.0% | 40.7% | GPT-5.2-Codex leads |
| AA-Omniscience Hallucination RateSource | 65.9% | 72.8% | Claude Sonnet 4.6 leads |
Math2 benchmarks
Multimodal3 benchmarks
Inst. Following1 benchmarks
| Benchmark | Claude Sonnet 4.6 | GPT-5.2-Codex | Result |
|---|---|---|---|
| AA-IFBenchSource | 41.2% | 77.6% | GPT-5.2-Codex leads |
Frequently Asked Questions (3)
Can I compare Claude Sonnet 4.6 and GPT-5.2-Codex 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 Claude Sonnet 4.6 and GPT-5.2-Codex today?
Claude Sonnet 4.6: $3.00 input / $15.00 output per 1M tokens GPT-5.2-Codex: $1.75 input / $14.00 output per 1M tokens Both model pages still include creator, context window, reasoning mode, and other metadata while benchmark coverage fills in.
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