Model comparison
GPT-5.3 Codex vs MAI-Thinking-1
Head-to-head evidence from 3 shared benchmark results across 2 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: GPT-5.3 Codex #26 (Supported); MAI-Thinking-1 unranked (Not scored). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GPT-5.3 Codex and MAI-Thinking-1 share 3 comparable benchmark results. 2 of 8 categories are comparable. 18 results are unique to GPT-5.3 Codex; 10 to MAI-Thinking-1.
Updated July 23, 2026- Shared results
- 3
- GPT-5.3 Codex only
- 18
- MAI-Thinking-1 only
- 10
- Comparable categories
- 2 / 8
Treat this as a split decision. GPT-5.3 Codex makes more sense if agentic is the priority or you need the larger 400K context window; MAI-Thinking-1 is the better fit if its strengths line up with your actual workload.
Confidence note. This is a partial-evidence comparison with 3 shared benchmark results across 2 evidence categories; 2 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
GPT-5.3 Codex and MAI-Thinking-1 finish on the same BenchAlign overall score, so this is less about a single winner and more about where the edge shows up. The BenchAlign headline says tie; the benchmark table is where the real choice happens.
GPT-5.3 Codex gives you the larger context window at 400K, compared with 256K for MAI-Thinking-1.
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-5.3 Codex | Δ | MAI-Thinking-1 |
|---|---|---|---|
| Agentic | GPT-5.3 Codex71.4 | Margin← 25.4 | MAI-Thinking-146.0 |
| Coding | GPT-5.3 Codex67.2 | Margin← 1.7 | MAI-Thinking-165.5 |
| Knowledge | GPT-5.3 CodexNot measured | MarginNo overlap | MAI-Thinking-172.5 |
| Math | GPT-5.3 CodexNot measured | MarginNo overlap | MAI-Thinking-189.7 |
| Inst. Following | GPT-5.3 CodexNot measured | MarginNo overlap | MAI-Thinking-185.0 |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
Terminal-Bench 2.0
AgenticA 77.3%B 46%Winner: GPT-5.3 CodexΔ 31.3Terminal-Bench 2.0: GPT-5.3 Codex scored 77.3%; MAI-Thinking-1 scored 46%. GPT-5.3 Codex wins this benchmark. - Source ↗
SWE-bench Verified
CodingA 85%B 73.5%Winner: GPT-5.3 CodexΔ 11.5SWE-bench Verified: GPT-5.3 Codex scored 85%; MAI-Thinking-1 scored 73.5%. GPT-5.3 Codex wins this benchmark. - Source ↗
SWE-bench Pro
CodingA 56.8%B 52.8%Winner: GPT-5.3 CodexΔ 4SWE-bench Pro: GPT-5.3 Codex scored 56.8%; MAI-Thinking-1 scored 52.8%. GPT-5.3 Codex wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | GPT-5.3 Codex | MAI-Thinking-1 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GPT-5.3 Codex$1.75 input / $14 output | MAI-Thinking-1Not available | A complete price comparison is not available. |
| Generation speedtokens per second | GPT-5.3 Codex79 tok/s | MAI-Thinking-1Not available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GPT-5.3 Codex88.26 s | MAI-Thinking-1Not available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GPT-5.3 Codex400K | MAI-Thinking-1256K | GPT-5.3 Codex lists the larger context window. |
Benchmark Deep Dive
AgenticGPT-5.3 Codex wins5 benchmarks
CodingGPT-5.3 Codex wins6 benchmarks
| Benchmark | GPT-5.3 Codex | MAI-Thinking-1 | Result |
|---|---|---|---|
| SWE-bench VerifiedSource | 85% | 73.5% | GPT-5.3 Codex leads |
| SWE-bench ProSource | 56.8% | 52.8% | GPT-5.3 Codex leads |
| SWE-RebenchSource | 58.2% | — | Not comparable |
| Vibe Code BenchSource | 61.77% | — | Not comparable |
| AA-SciCodeSource | 53.2% | — | Not comparable |
| Terminal-Bench 2.0Source | — | 46.0% | Not comparable |
Reasoning3 benchmarks
Knowledge10 benchmarks
| Benchmark | GPT-5.3 Codex | MAI-Thinking-1 | Result |
|---|---|---|---|
| Artificial Analysis Intelligence IndexSource | 44.3% | — | Not comparable |
| AA-GPQA DiamondSource | 91.5% | — | Not comparable |
| AA-HLESource | 39.9% | — | Not comparable |
| AA-Omniscience IndexSource | 9.9% | — | Not comparable |
| AA-Omniscience AccuracySource | 51.8% | — | Not comparable |
| AA-Omniscience Hallucination RateSource | 86.9% | — | Not comparable |
| GPQASource | — | 84.2% | Not comparable |
| GPQA-DSource | — | 84.2% | Not comparable |
| MMLU-ProSource | — | 85% | Not comparable |
| SimpleQASource | — | 31% | Not comparable |
Math3 benchmarks
Multimodal2 benchmarks
Frequently Asked Questions (3)
Which is better, GPT-5.3 Codex or MAI-Thinking-1?
GPT-5.3 Codex and MAI-Thinking-1 are tied on the BenchAlign overall score, so the right pick depends on which category matters most for your use case.
Which is better for coding, GPT-5.3 Codex or MAI-Thinking-1?
GPT-5.3 Codex has the edge for coding in this comparison, averaging 67.2 versus 65.5. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
Which is better for agentic tasks, GPT-5.3 Codex or MAI-Thinking-1?
GPT-5.3 Codex has the edge for agentic tasks in this comparison, averaging 71.4 versus 46. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
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