Model comparison
GPT-5.5 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-5.5 #11 (Estimated); Laguna M.1 unranked (Not scored). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GPT-5.5 and Laguna M.1 share 3 comparable benchmark results. 2 of 8 categories are comparable. 50 results are unique to GPT-5.5; 2 to Laguna M.1.
Updated July 27, 2026- Shared results
- 3
- GPT-5.5 only
- 50
- Laguna M.1 only
- 2
- Comparable categories
- 2 / 8
Treat this as a split decision. GPT-5.5 makes more sense if agentic is the priority or you need the larger 1M context window; Laguna M.1 is the better fit if coding is the priority.
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-5.5 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-5.5 gives you the larger context window at 1M, 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-5.5 | Δ | Laguna M.1 |
|---|---|---|---|
| Agentic | GPT-5.581.6 | Margin← 35.8 | Laguna M.145.8 |
| Coding | GPT-5.558.6 | Margin→ 6.2 | Laguna M.164.8 |
| Reasoning | GPT-5.585.0 | MarginNo overlap | Laguna M.1Not measured |
| Knowledge | GPT-5.557.8 | MarginNo overlap | Laguna M.1Not measured |
| Math | GPT-5.547.6 | MarginNo overlap | Laguna M.1Not measured |
| Multimodal | GPT-5.570.4 | 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 82%B 45.8%Winner: GPT-5.5Δ 36.2Terminal-Bench 2.0: GPT-5.5 scored 82%; Laguna M.1 scored 45.8%. GPT-5.5 wins this benchmark. - Source ↗
SWE-bench Pro
CodingA 58.6%B 49.2%Winner: GPT-5.5Δ 9.4SWE-bench Pro: GPT-5.5 scored 58.6%; Laguna M.1 scored 49.2%. GPT-5.5 wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | GPT-5.5 | Laguna M.1 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GPT-5.5$5 input / $30 output | Laguna M.1Not available | A complete price comparison is not available. |
| Generation speedtokens per second | GPT-5.5Not available | Laguna M.1Not available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GPT-5.5Not available | Laguna M.1Not available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GPT-5.51M | Laguna M.1256K | GPT-5.5 lists the larger context window. |
Benchmark Deep Dive
AgenticGPT-5.5 wins19 benchmarks
| Benchmark | GPT-5.5 | Laguna M.1 | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 82% | 45.8% | GPT-5.5 leads |
| CyberGymSource | 81.8% | — | Not comparable |
| BrowseCompSource | 84.4% | — | Not comparable |
| OSWorld-VerifiedSource | 78.7% | — | Not comparable |
| MCP AtlasSource | 75.3% | — | Not comparable |
| ToolathlonSource | 55.6% | — | Not comparable |
| τ²-bench resultsSource | 93.9% | — | Not comparable |
| AA Agentic IndexSource | 44.9% | — | Not comparable |
| APEX-Agents-AASource | 37.7% | — | Not comparable |
| GDPval-AASource | 49.6% | — | Not comparable |
| GDPval-AASource | 1491 | — | Not comparable |
| Gert LabsSource | 72.93% | — | Not comparable |
| ResearchClawBenchSource | 17.0% | — | Not comparable |
| OSWorld 2.0Source | 13.0% | — | Not comparable |
| JobBenchSource | 42.7% | — | Not comparable |
| ExploitGymSource | 13.4% | — | Not comparable |
| AA AutomationBenchSource | 42.1% | — | Not comparable |
| AA EnterpriseOps-GymSource | 46.6% | — | Not comparable |
| AA ITBenchSource | 45.8% | — | Not comparable |
CodingLaguna M.1 wins11 benchmarks
| Benchmark | GPT-5.5 | Laguna M.1 | Result |
|---|---|---|---|
| SWE-bench ProSource | 58.6% | 49.2% | GPT-5.5 leads |
| Terminal-Bench 2.0Source | 82.0% | 45.8% | GPT-5.5 leads |
| Vibe Code BenchSource | 69.85% | — | Not comparable |
| React Native EvalsSource | 84.7% | — | Not comparable |
| cursorBench31Source | 59.2% | — | Not comparable |
| cursorBench32Source | 58.4% | — | Not comparable |
| AA Coding IndexSource | 74.9% | — | Not comparable |
| AA-SciCodeSource | 56.1% | — | Not comparable |
| FrontierCode 1.1 MainSource | 43.0% | — | Not comparable |
| SWE-bench VerifiedSource | — | 74.6% | Not comparable |
| SWE MultilingualSource | — | 63.1% | Not comparable |
Reasoning6 benchmarks
Knowledge10 benchmarks
| Benchmark | GPT-5.5 | Laguna M.1 | Result |
|---|---|---|---|
| GPQASource | 93.6% | — | Not comparable |
| GPQA-DSource | 93.6% | — | Not comparable |
| HLESource | 52.2% | — | Not comparable |
| HLE w/o toolsSource | 41.4% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 54.8% | — | Not comparable |
| AA-GPQA DiamondSource | 93.5% | — | Not comparable |
| AA-HLESource | 44.3% | — | Not comparable |
| AA-Omniscience IndexSource | 20.1% | — | Not comparable |
| AA-Omniscience AccuracySource | 56.9% | — | Not comparable |
| AA-Omniscience Hallucination RateSource | 85.5% | — | Not comparable |
Math3 benchmarks
Multimodal5 benchmarks
Inst. Following1 benchmarks
| Benchmark | GPT-5.5 | Laguna M.1 | Result |
|---|---|---|---|
| AA-IFBenchSource | 75.9% | — | Not comparable |
Frequently Asked Questions (3)
Which is better, GPT-5.5 or Laguna M.1?
GPT-5.5 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-5.5 or Laguna M.1?
Laguna M.1 has the edge for coding in this comparison, averaging 64.8 versus 58.6. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
Which is better for agentic tasks, GPT-5.5 or Laguna M.1?
GPT-5.5 has the edge for agentic tasks in this comparison, averaging 81.6 versus 45.8. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
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