Model comparison
Llama 3.1 405B vs Ornith-1.0-397B
Head-to-head evidence from 0 shared benchmark results across 0 categories. Overall scores shown here use BenchLM's provisional ranking lane.
Evidence parity. Llama 3.1 405B and Ornith-1.0-397B share 0 comparable benchmark results. 0 of 8 categories are comparable. 12 results are unique to Llama 3.1 405B; 7 to Ornith-1.0-397B.
Updated July 14, 2026- Shared results
- 0
- Llama 3.1 405B only
- 12
- Ornith-1.0-397B only
- 7
- Comparable categories
- 0 / 8
Benchmark data for Llama 3.1 405B and Ornith-1.0-397B is coming soon on BenchLM.
Confidence note. This is a partial-evidence comparison with 0 shared benchmark results across 0 evidence categories; 0 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
BenchLM has partial data for these models, but not enough overlapping benchmark coverage to produce a fair score-level comparison yet.
Ornith-1.0-397B has the larger context window at 256K, compared with 128K for Llama 3.1 405B.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | Llama 3.1 405B | Ornith-1.0-397B | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | Llama 3.1 405B$0 input / $0 output | Ornith-1.0-397B$0 input / $0 output | Listed prices are equal. |
| Generation speedtokens per second | Llama 3.1 405B29 tok/s | Ornith-1.0-397BNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | Llama 3.1 405B2.19 s | Ornith-1.0-397BNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | Llama 3.1 405B128K | Ornith-1.0-397B256K | Ornith-1.0-397B lists the larger context window. |
Benchmark Deep Dive
Agentic3 benchmarks
Coding7 benchmarks
| Benchmark | Llama 3.1 405B | Ornith-1.0-397B | Result |
|---|---|---|---|
| Terminal-Bench HardSource | 6.8% | — | Not comparable |
| AA-SciCodeSource | 29.9% | — | Not comparable |
| SWE-bench VerifiedSource | — | 82.4% | Not comparable |
| SWE-bench ProSource | — | 62.2% | Not comparable |
| SWE MultilingualSource | — | 78.9% | Not comparable |
| NL2RepoSource | — | 48.2% | Not comparable |
| Terminal-Bench 2.0Source | — | 77.5% | Not comparable |
Reasoning2 benchmarks
Knowledge6 benchmarks
| Benchmark | Llama 3.1 405B | Ornith-1.0-397B | Result |
|---|---|---|---|
| Artificial Analysis Intelligence IndexSource | 8.5% | — | Not comparable |
| AA-GPQA DiamondSource | 51.5% | — | Not comparable |
| AA-HLESource | 4.2% | — | Not comparable |
| AA-Omniscience IndexSource | -17.3% | — | Not comparable |
| AA-Omniscience AccuracySource | 22.3% | — | Not comparable |
| AA-Omniscience Hallucination RateSource | 51.0% | — | Not comparable |
Inst. Following1 benchmarks
| Benchmark | Llama 3.1 405B | Ornith-1.0-397B | Result |
|---|---|---|---|
| AA-IFBenchSource | 39.0% | — | Not comparable |
Frequently Asked Questions (3)
Can I compare Llama 3.1 405B and Ornith-1.0-397B on BenchLM yet?
Not fully yet. BenchLM is tracking both models, but the sourced benchmark breakdown for this comparison is still coming soon.
Why does this comparison show “coming soon”?
BenchLM only shows category winners and benchmark-level calls when we have sourced results that can be compared fairly. For these models, the public benchmark coverage is not complete enough yet.
What data is available for Llama 3.1 405B and Ornith-1.0-397B today?
Llama 3.1 405B: $0.00 input / $0.00 output per 1M tokens Ornith-1.0-397B: $0.00 input / $0.00 output per 1M tokens Both model pages still include creator, context window, reasoning mode, and other metadata while benchmark coverage fills in.
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