Model comparison
GPT-5.5 vs LFM2.5-VL-450M
Head-to-head evidence from 1 shared benchmark result across 1 category. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: GPT-5.5 #9 (Estimated); LFM2.5-VL-450M unranked (Not scored). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GPT-5.5 and LFM2.5-VL-450M share 1 comparable benchmark result. 1 of 8 categories are comparable. 56 results are unique to GPT-5.5; 6 to LFM2.5-VL-450M.
Updated July 22, 2026- Shared results
- 1
- GPT-5.5 only
- 56
- LFM2.5-VL-450M only
- 6
- Comparable categories
- 1 / 8
Treat this as a split decision. GPT-5.5 makes more sense if knowledge is the priority or you need the larger 1M context window; LFM2.5-VL-450M is the better fit if you want the cheaper token bill or you would rather avoid the extra latency and token burn of a reasoning model.
Confidence note. This is a partial-evidence comparison with 1 shared benchmark result across 1 evidence category; 1 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 LFM2.5-VL-450M 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 is also the more expensive model on tokens at $5.00 input / $30.00 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for LFM2.5-VL-450M. That is roughly Infinityx on output cost alone. GPT-5.5 is the reasoning model in the pair, while LFM2.5-VL-450M is not. That usually helps on harder chain-of-thought-heavy tests, but it can also mean more latency and more token spend in real use. GPT-5.5 gives you the larger context window at 1M, compared with 128K for LFM2.5-VL-450M.
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 | LFM2.5-VL-450M |
|---|---|---|---|
| Knowledge | GPT-5.557.8 | Margin← 37.3 | LFM2.5-VL-450M20.5 |
| Agentic | GPT-5.581.6 | MarginNo overlap | LFM2.5-VL-450MNot measured |
| Coding | GPT-5.558.6 | MarginNo overlap | LFM2.5-VL-450MNot measured |
| Reasoning | GPT-5.585.0 | MarginNo overlap | LFM2.5-VL-450MNot measured |
| Math | GPT-5.547.6 | MarginNo overlap | LFM2.5-VL-450MNot measured |
| Multimodal | GPT-5.570.4 | MarginNo overlap | LFM2.5-VL-450MNot measured |
| Inst. Following | GPT-5.5Not measured | MarginNo overlap | LFM2.5-VL-450M61.2 |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
GPQA
KnowledgeA 93.6%B 25.7%Winner: GPT-5.5Δ 67.9GPQA: GPT-5.5 scored 93.6%; LFM2.5-VL-450M scored 25.7%. 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 | LFM2.5-VL-450M | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GPT-5.5$5 input / $30 output | LFM2.5-VL-450M$0 input / $0 output | LFM2.5-VL-450M has the lower combined listed price. |
| Generation speedtokens per second | GPT-5.5Not available | LFM2.5-VL-450MNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GPT-5.5Not available | LFM2.5-VL-450MNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GPT-5.51M | LFM2.5-VL-450M128K | GPT-5.5 lists the larger context window. |
Benchmark Deep Dive
Agentic25 benchmarks
| Benchmark | GPT-5.5 | LFM2.5-VL-450M | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 82% | — | Not comparable |
| 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 | 49.5% | — | Not comparable |
| GDPval-AASource | 1490 | — | 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 BriefcaseSource | 1154 | — | Not comparable |
| AA AutomationBenchSource | 42.1% | — | Not comparable |
| AA EnterpriseOps-GymSource | 46.6% | — | Not comparable |
| AA Harvey LABSource | 86.3% | — | Not comparable |
| AA ITBenchSource | 45.8% | — | Not comparable |
| AA Tau3 BankingSource | 31.3% | — | Not comparable |
| terminalBenchHardSource | 60.6% | — | Not comparable |
| aaTerminalBench21Source | 84.3% | — | Not comparable |
| BFCL v4Source | — | 21.1% | Not comparable |
Coding9 benchmarks
| Benchmark | GPT-5.5 | LFM2.5-VL-450M | Result |
|---|---|---|---|
| SWE-bench ProSource | 58.6% | — | Not comparable |
| Terminal-Bench 2.0Source | 82.0% | — | Not comparable |
| 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 |
Reasoning5 benchmarks
KnowledgeGPT-5.5 wins11 benchmarks
| Benchmark | GPT-5.5 | LFM2.5-VL-450M | Result |
|---|---|---|---|
| GPQASource | 93.6% | 25.7% | GPT-5.5 leads |
| 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 |
| MMLU-ProSource | — | 19.3% | Not comparable |
Math3 benchmarks
Multimodal8 benchmarks
| Benchmark | GPT-5.5 | LFM2.5-VL-450M | Result |
|---|---|---|---|
| MMMU-ProSource | 81.2% | — | Not comparable |
| MMMU-Pro w/ PythonSource | 83.2% | — | Not comparable |
| OfficeQA ProSource | 54.1% | — | Not comparable |
| AA-MMMU-ProSource | 79.9% | — | Not comparable |
| Design Arena WebsiteSource | 1282 | — | Not comparable |
| MMMUSource | — | 32.7% | Not comparable |
| RealWorldQASource | — | 58.4% | Not comparable |
| CountBenchSource | — | 73.3% | Not comparable |
Frequently Asked Questions (2)
Which is better, GPT-5.5 or LFM2.5-VL-450M?
GPT-5.5 and LFM2.5-VL-450M 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 knowledge tasks, GPT-5.5 or LFM2.5-VL-450M?
GPT-5.5 has the edge for knowledge tasks in this comparison, averaging 57.8 versus 20.5. Inside this category, GPQA is the benchmark that creates the most daylight between them.
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