Model comparison
GPT-5.4 vs Laguna M.1
Head-to-head evidence from 2 shared benchmark results across 2 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: GPT-5.4 #10 (Supported); Laguna M.1 unranked (Not scored). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GPT-5.4 and Laguna M.1 share 2 comparable benchmark results. 2 of 8 categories are comparable. 52 results are unique to GPT-5.4; 3 to Laguna M.1.
Updated July 27, 2026- Shared results
- 2
- GPT-5.4 only
- 52
- Laguna M.1 only
- 3
- Comparable categories
- 2 / 8
Treat this as a split decision. GPT-5.4 makes more sense if agentic is the priority or you need the larger 1.05M context window; Laguna M.1 is the better fit if coding is the priority.
Confidence note. This is a partial-evidence comparison with 2 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-5.4 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-5.4 gives you the larger context window at 1.05M, 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-5.4 | Δ | Laguna M.1 |
|---|---|---|---|
| Agentic | GPT-5.477.2 | Margin← 31.4 | Laguna M.145.8 |
| Coding | GPT-5.457.7 | Margin→ 7.1 | Laguna M.164.8 |
| Reasoning | GPT-5.474.0 | MarginNo overlap | Laguna M.1Not measured |
| Knowledge | GPT-5.457.6 | MarginNo overlap | Laguna M.1Not measured |
| Math | GPT-5.442.5 | MarginNo overlap | Laguna M.1Not measured |
| Multimodal | GPT-5.473.2 | MarginNo overlap | Laguna M.1Not measured |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
Terminal-Bench 2.0
AgenticA 75.1%B 45.8%Winner: GPT-5.4Δ 29.3Terminal-Bench 2.0: GPT-5.4 scored 75.1%; Laguna M.1 scored 45.8%. GPT-5.4 wins this benchmark. - Source ↗
SWE-bench Pro
CodingA 57.7%B 49.2%Winner: GPT-5.4Δ 8.5SWE-bench Pro: GPT-5.4 scored 57.7%; Laguna M.1 scored 49.2%. GPT-5.4 wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | GPT-5.4 | Laguna M.1 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GPT-5.4$2.5 input / $15 output | Laguna M.1Not available | A complete price comparison is not available. |
| Generation speedtokens per second | GPT-5.474 tok/s | Laguna M.1Not available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GPT-5.4151.79 s | Laguna M.1Not available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GPT-5.41.05M | Laguna M.1256K | GPT-5.4 lists the larger context window. |
Benchmark Deep Dive
AgenticGPT-5.4 wins17 benchmarks
| Benchmark | GPT-5.4 | Laguna M.1 | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 75.1% | 45.8% | GPT-5.4 leads |
| CyberGymSource | 79.0% | — | Not comparable |
| BrowseCompSource | 82.7% | — | Not comparable |
| OSWorld-VerifiedSource | 75% | — | Not comparable |
| MCP AtlasSource | 70.6% | — | Not comparable |
| ToolathlonSource | 54.6% | — | Not comparable |
| τ²-bench resultsSource | 87.1% | — | Not comparable |
| Claw-EvalSource | 60.3% | — | Not comparable |
| DeepSearchQASource | 73.6% | — | Not comparable |
| AA Agentic IndexSource | 41.1% | — | Not comparable |
| APEX-Agents-AASource | 33.3% | — | Not comparable |
| GDPval-AASource | 44.6% | — | Not comparable |
| GDPval-AASource | 1392 | — | Not comparable |
| Gert LabsSource | 64.89% | — | Not comparable |
| ResearchClawBenchSource | 15.3% | — | Not comparable |
| JobBenchSource | 38.9% | — | Not comparable |
| ExploitGymSource | 6.0% | — | Not comparable |
CodingLaguna M.1 wins9 benchmarks
| Benchmark | GPT-5.4 | Laguna M.1 | Result |
|---|---|---|---|
| LiveCodeBench ProSource | 87.5% | — | Not comparable |
| SWE-bench ProSource | 57.7% | 49.2% | GPT-5.4 leads |
| React Native EvalsSource | 85.3% | — | Not comparable |
| Vibe Code BenchSource | 67.42% | — | Not comparable |
| AA Coding IndexSource | 71.0% | — | Not comparable |
| AA-SciCodeSource | 56.6% | — | Not comparable |
| SWE-bench VerifiedSource | — | 74.6% | Not comparable |
| SWE MultilingualSource | — | 63.1% | Not comparable |
| Terminal-Bench 2.0Source | — | 45.8% | Not comparable |
Reasoning4 benchmarks
Knowledge13 benchmarks
| Benchmark | GPT-5.4 | Laguna M.1 | Result |
|---|---|---|---|
| GPQASource | 92.8% | — | Not comparable |
| HLESource | 52.1% | — | Not comparable |
| HLE w/o toolsSource | 39.8% | — | Not comparable |
| GPQA-DSource | 92.8% | — | Not comparable |
| HealthBench HardSource | 40.1% | — | Not comparable |
| MedXpertQA (Text)Source | 59.6% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 51.4% | — | Not comparable |
| AA-GPQA DiamondSource | 92.0% | — | Not comparable |
| AA-HLESource | 41.6% | — | Not comparable |
| AA-Omniscience IndexSource | 5.7% | — | Not comparable |
| AA-Omniscience AccuracySource | 50.0% | — | Not comparable |
| AA-Omniscience Hallucination RateSource | 88.6% | — | Not comparable |
| HealthBench ProfessionalSource | 48.1% | — | Not comparable |
Math2 benchmarks
Multimodal11 benchmarks
| Benchmark | GPT-5.4 | Laguna M.1 | Result |
|---|---|---|---|
| MMMU-ProSource | 81.2% | — | Not comparable |
| OfficeQA ProSource | 53.2% | — | Not comparable |
| MMMU-Pro w/ PythonSource | 82.1% | — | Not comparable |
| CharXivSource | 82.8% | — | Not comparable |
| ERQASource | 65.4% | — | Not comparable |
| SimpleVQASource | 61.1% | — | Not comparable |
| ScreenSpot ProSource | 85.4% | — | Not comparable |
| ZeroBenchSource | 41.0% | — | Not comparable |
| MedXpertQA (MM)Source | 77.1% | — | Not comparable |
| AA-MMMU-ProSource | 78.4% | — | Not comparable |
| Design Arena WebsiteSource | 1245 | — | Not comparable |
Inst. Following1 benchmarks
| Benchmark | GPT-5.4 | Laguna M.1 | Result |
|---|---|---|---|
| AA-IFBenchSource | 73.9% | — | Not comparable |
Frequently Asked Questions (3)
Which is better, GPT-5.4 or Laguna M.1?
GPT-5.4 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-5.4 or Laguna M.1?
Laguna M.1 has the edge for coding in this comparison, averaging 64.8 versus 57.7. Inside this category, SWE-bench Pro is the benchmark that creates the most daylight between them.
Which is better for agentic tasks, GPT-5.4 or Laguna M.1?
GPT-5.4 has the edge for agentic tasks in this comparison, averaging 77.2 versus 45.8. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
Related Comparisons
Explore More
Know when it’s worth switching models
The model to choose, the cheaper alternative, and the release we would wait on.
One email each week. Unsubscribe anytime.