Model comparison
GPT-5.5 vs Ornith-1.0-397B
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 #9 (Estimated); Ornith-1.0-397B unranked (Not scored). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GPT-5.5 and Ornith-1.0-397B share 3 comparable benchmark results. 2 of 8 categories are comparable. 54 results are unique to GPT-5.5; 4 to Ornith-1.0-397B.
Updated July 23, 2026- Shared results
- 3
- GPT-5.5 only
- 54
- Ornith-1.0-397B only
- 4
- 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; Ornith-1.0-397B 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 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 Ornith-1.0-397B 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 Ornith-1.0-397B. That is roughly Infinityx on output cost alone. GPT-5.5 gives you the larger context window at 1M, compared with 256K for Ornith-1.0-397B.
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 | Ornith-1.0-397B |
|---|---|---|---|
| Coding | GPT-5.558.6 | Margin→ 16.0 | Ornith-1.0-397B74.6 |
| Agentic | GPT-5.581.6 | Margin← 4.1 | Ornith-1.0-397B77.5 |
| Reasoning | GPT-5.585.0 | MarginNo overlap | Ornith-1.0-397BNot measured |
| Knowledge | GPT-5.557.8 | MarginNo overlap | Ornith-1.0-397BNot measured |
| Math | GPT-5.547.6 | MarginNo overlap | Ornith-1.0-397BNot measured |
| Multimodal | GPT-5.570.4 | MarginNo overlap | Ornith-1.0-397BNot 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 77.5%Winner: GPT-5.5Δ 4.5Terminal-Bench 2.0: GPT-5.5 scored 82%; Ornith-1.0-397B scored 77.5%. GPT-5.5 wins this benchmark. - Source ↗
SWE-bench Pro
CodingA 58.6%B 62.2%Winner: Ornith-1.0-397BΔ 3.6SWE-bench Pro: GPT-5.5 scored 58.6%; Ornith-1.0-397B scored 62.2%. Ornith-1.0-397B wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | GPT-5.5 | Ornith-1.0-397B | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GPT-5.5$5 input / $30 output | Ornith-1.0-397B$0 input / $0 output | Ornith-1.0-397B has the lower combined listed price. |
| Generation speedtokens per second | GPT-5.5Not available | Ornith-1.0-397BNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GPT-5.5Not available | Ornith-1.0-397BNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GPT-5.51M | Ornith-1.0-397B256K | GPT-5.5 lists the larger context window. |
Benchmark Deep Dive
AgenticGPT-5.5 wins25 benchmarks
| Benchmark | GPT-5.5 | Ornith-1.0-397B | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 82% | 77.5% | 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 | 49.5% | — | 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 |
| Claw-EvalSource | — | 77.1% | Not comparable |
CodingOrnith-1.0-397B wins12 benchmarks
| Benchmark | GPT-5.5 | Ornith-1.0-397B | Result |
|---|---|---|---|
| SWE-bench ProSource | 58.6% | 62.2% | Ornith-1.0-397B leads |
| Terminal-Bench 2.0Source | 82.0% | 77.5% | 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 | — | 82.4% | Not comparable |
| SWE MultilingualSource | — | 78.9% | Not comparable |
| NL2RepoSource | — | 48.2% | Not comparable |
Reasoning5 benchmarks
Knowledge10 benchmarks
| Benchmark | GPT-5.5 | Ornith-1.0-397B | 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 | Ornith-1.0-397B | Result |
|---|---|---|---|
| AA-IFBenchSource | 75.9% | — | Not comparable |
Frequently Asked Questions (3)
Which is better, GPT-5.5 or Ornith-1.0-397B?
GPT-5.5 and Ornith-1.0-397B 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 Ornith-1.0-397B?
Ornith-1.0-397B has the edge for coding in this comparison, averaging 74.6 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 Ornith-1.0-397B?
GPT-5.5 has the edge for agentic tasks in this comparison, averaging 81.6 versus 77.5. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
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