Model comparison
GPT-5.3 Codex vs Laguna M.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 #30 (Supported); Laguna M.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 Laguna M.1 share 3 comparable benchmark results. 2 of 8 categories are comparable. 18 results are unique to GPT-5.3 Codex; 2 to Laguna M.1.
Updated July 27, 2026- Shared results
- 3
- GPT-5.3 Codex only
- 18
- Laguna M.1 only
- 2
- 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; Laguna M.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 Laguna M.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 Laguna M.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 | Laguna M.1 |
|---|---|---|---|
| Agentic | GPT-5.3 Codex71.4 | Margin← 25.6 | Laguna M.145.8 |
| Coding | GPT-5.3 Codex67.2 | Margin← 2.4 | Laguna M.164.8 |
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 45.8%Winner: GPT-5.3 CodexΔ 31.5Terminal-Bench 2.0: GPT-5.3 Codex scored 77.3%; Laguna M.1 scored 45.8%. GPT-5.3 Codex wins this benchmark. - Source ↗
SWE-bench Verified
CodingA 85%B 74.6%Winner: GPT-5.3 CodexΔ 10.4SWE-bench Verified: GPT-5.3 Codex scored 85%; Laguna M.1 scored 74.6%. GPT-5.3 Codex wins this benchmark. - Source ↗
SWE-bench Pro
CodingA 56.8%B 49.2%Winner: GPT-5.3 CodexΔ 7.6SWE-bench Pro: GPT-5.3 Codex scored 56.8%; Laguna M.1 scored 49.2%. 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 | Laguna M.1 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GPT-5.3 Codex$1.75 input / $14 output | Laguna M.1Not available | A complete price comparison is not available. |
| Generation speedtokens per second | GPT-5.3 Codex79 tok/s | Laguna M.1Not available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GPT-5.3 Codex88.26 s | Laguna M.1Not available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GPT-5.3 Codex400K | Laguna M.1256K | GPT-5.3 Codex lists the larger context window. |
Benchmark Deep Dive
AgenticGPT-5.3 Codex wins5 benchmarks
CodingGPT-5.3 Codex wins7 benchmarks
| Benchmark | GPT-5.3 Codex | Laguna M.1 | Result |
|---|---|---|---|
| SWE-bench VerifiedSource | 85% | 74.6% | GPT-5.3 Codex leads |
| SWE-bench ProSource | 56.8% | 49.2% | GPT-5.3 Codex leads |
| SWE-RebenchSource | 58.2% | — | Not comparable |
| Vibe Code BenchSource | 61.77% | — | Not comparable |
| AA-SciCodeSource | 53.2% | — | Not comparable |
| SWE MultilingualSource | — | 63.1% | Not comparable |
| Terminal-Bench 2.0Source | — | 45.8% | Not comparable |
Reasoning2 benchmarks
Knowledge6 benchmarks
| Benchmark | GPT-5.3 Codex | Laguna M.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 |
Multimodal2 benchmarks
Inst. Following1 benchmarks
| Benchmark | GPT-5.3 Codex | Laguna M.1 | Result |
|---|---|---|---|
| AA-IFBenchSource | 75.4% | — | Not comparable |
Frequently Asked Questions (3)
Which is better, GPT-5.3 Codex or Laguna M.1?
GPT-5.3 Codex and Laguna M.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 Laguna M.1?
GPT-5.3 Codex has the edge for coding in this comparison, averaging 67.2 versus 64.8. 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 Laguna M.1?
GPT-5.3 Codex has the edge for agentic tasks in this comparison, averaging 71.4 versus 45.8. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
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