Model comparison
GPT-5.4 vs Laguna S 2.1
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 #8 (Supported); Laguna S 2.1 unranked (Not scored). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GPT-5.4 and Laguna S 2.1 share 2 comparable benchmark results. 2 of 8 categories are comparable. 50 results are unique to GPT-5.4; 4 to Laguna S 2.1.
Updated July 21, 2026- Shared results
- 2
- GPT-5.4 only
- 50
- Laguna S 2.1 only
- 4
- 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 S 2.1 is the better fit if coding is the priority or you want the cheaper token bill.
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 S 2.1 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 is also the more expensive model on tokens at $2.50 input / $15.00 output per 1M tokens, versus $0.10 input / $0.20 output per 1M tokens for Laguna S 2.1. That is roughly 75.0x on output cost alone. GPT-5.4 gives you the larger context window at 1.05M, compared with 1M for Laguna S 2.1.
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 S 2.1 |
|---|---|---|---|
| Agentic | GPT-5.477.2 | Margin← 7.0 | Laguna S 2.170.2 |
| Coding | GPT-5.457.7 | Margin→ 1.7 | Laguna S 2.159.4 |
| Knowledge | GPT-5.457.6 | MarginNo overlap | Laguna S 2.1Not measured |
| Math | GPT-5.442.5 | MarginNo overlap | Laguna S 2.1Not measured |
| Multimodal | GPT-5.473.2 | MarginNo overlap | Laguna S 2.1Not 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 70.2%Winner: GPT-5.4Δ 4.9Terminal-Bench 2.0: GPT-5.4 scored 75.1%; Laguna S 2.1 scored 70.2%. GPT-5.4 wins this benchmark. - Source ↗
SWE-bench Pro
CodingA 57.7%B 59.4%Winner: Laguna S 2.1Δ 1.7SWE-bench Pro: GPT-5.4 scored 57.7%; Laguna S 2.1 scored 59.4%. Laguna S 2.1 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 S 2.1 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GPT-5.4$2.5 input / $15 output | Laguna S 2.1$0.1 input / $0.2 output | Laguna S 2.1 has the lower combined listed price. |
| Generation speedtokens per second | GPT-5.474 tok/s | Laguna S 2.1Not available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GPT-5.4151.79 s | Laguna S 2.1Not available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GPT-5.41.05M | Laguna S 2.11M | GPT-5.4 lists the larger context window. |
Benchmark Deep Dive
AgenticGPT-5.4 wins18 benchmarks
| Benchmark | GPT-5.4 | Laguna S 2.1 | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 75.1% | 70.2% | 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 | 44.7% | — | Not comparable |
| GDPval-AASource | 1395 | — | Not comparable |
| Gert LabsSource | 64.89% | — | Not comparable |
| ResearchClawBenchSource | 15.3% | — | Not comparable |
| JobBenchSource | 38.9% | — | Not comparable |
| ExploitGymSource | 6.0% | — | Not comparable |
| Toolathlon-VerifiedSource | — | 49.7% | Not comparable |
CodingLaguna S 2.1 wins9 benchmarks
| Benchmark | GPT-5.4 | Laguna S 2.1 | Result |
|---|---|---|---|
| LiveCodeBench ProSource | 87.5% | — | Not comparable |
| SWE-bench ProSource | 57.7% | 59.4% | Laguna S 2.1 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 |
| Terminal-Bench 2.0Source | — | 70.2% | Not comparable |
| SWE MultilingualSource | — | 78.5% | Not comparable |
| deepSweSource | — | 40.4% | Not comparable |
Reasoning2 benchmarks
Knowledge13 benchmarks
| Benchmark | GPT-5.4 | Laguna S 2.1 | 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 S 2.1 | 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 | 1250 | — | Not comparable |
Inst. Following1 benchmarks
| Benchmark | GPT-5.4 | Laguna S 2.1 | Result |
|---|---|---|---|
| AA-IFBenchSource | 73.9% | — | Not comparable |
Frequently Asked Questions (3)
Which is better, GPT-5.4 or Laguna S 2.1?
GPT-5.4 and Laguna S 2.1 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 S 2.1?
Laguna S 2.1 has the edge for coding in this comparison, averaging 59.4 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 S 2.1?
GPT-5.4 has the edge for agentic tasks in this comparison, averaging 77.2 versus 70.2. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
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