Model comparison
DeepSeek V3 vs Ornith-1.0-397B
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: DeepSeek V3 #147 (Supported); Ornith-1.0-397B unranked (Not scored). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. DeepSeek V3 and Ornith-1.0-397B share 1 comparable benchmark result. 1 of 8 categories are comparable. 21 results are unique to DeepSeek V3; 6 to Ornith-1.0-397B.
Updated July 18, 2026- Shared results
- 1
- DeepSeek V3 only
- 21
- Ornith-1.0-397B only
- 6
- Comparable categories
- 1 / 8
Treat this as a split decision. DeepSeek V3 makes more sense if you would rather avoid the extra latency and token burn of a reasoning model; 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 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
DeepSeek V3 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.
DeepSeek V3 is also the more expensive model on tokens at $0.27 input / $1.10 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. Ornith-1.0-397B is the reasoning model in the pair, while DeepSeek V3 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. Ornith-1.0-397B gives you the larger context window at 256K, compared with 128K for DeepSeek V3.
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 | DeepSeek V3 | Δ | Ornith-1.0-397B |
|---|---|---|---|
| Coding | DeepSeek V338.9 | Margin→ 35.7 | Ornith-1.0-397B74.6 |
| Agentic | DeepSeek V3Not measured | MarginNo overlap | Ornith-1.0-397B77.5 |
| Knowledge | DeepSeek V372.7 | MarginNo overlap | Ornith-1.0-397BNot measured |
| Math | DeepSeek V31.7 | MarginNo overlap | Ornith-1.0-397BNot measured |
| Inst. Following | DeepSeek V386.1 | MarginNo overlap | Ornith-1.0-397BNot measured |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
SWE-bench Verified
CodingA 42%B 82.4%Winner: Ornith-1.0-397BΔ 40.4SWE-bench Verified: DeepSeek V3 scored 42%; Ornith-1.0-397B scored 82.4%. 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 | DeepSeek V3 | Ornith-1.0-397B | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | DeepSeek V3$0.27 input / $1.1 output | Ornith-1.0-397B$0 input / $0 output | Ornith-1.0-397B has the lower combined listed price. |
| Generation speedtokens per second | DeepSeek V3Not available | Ornith-1.0-397BNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | DeepSeek V3Not available | Ornith-1.0-397BNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | DeepSeek V3128K | Ornith-1.0-397B256K | Ornith-1.0-397B lists the larger context window. |
Benchmark Deep Dive
Agentic6 benchmarks
CodingOrnith-1.0-397B wins8 benchmarks
| Benchmark | DeepSeek V3 | Ornith-1.0-397B | Result |
|---|---|---|---|
| LiveCodeBenchSource | 37.6% | — | Not comparable |
| SWE-bench VerifiedSource | 42% | 82.4% | Ornith-1.0-397B leads |
| AA Coding IndexSource | 23.0% | — | Not comparable |
| AA-SciCodeSource | 35.4% | — | 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
Knowledge8 benchmarks
| Benchmark | DeepSeek V3 | Ornith-1.0-397B | Result |
|---|---|---|---|
| GPQASource | 59.1% | — | Not comparable |
| MMLU-ProSource | 75.9% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 14.2% | — | Not comparable |
| AA-GPQA DiamondSource | 55.7% | — | Not comparable |
| AA-HLESource | 3.6% | — | Not comparable |
| AA-Omniscience IndexSource | -41.3% | — | Not comparable |
| AA-Omniscience AccuracySource | 25.4% | — | Not comparable |
| AA-Omniscience Hallucination RateSource | 89.4% | — | Not comparable |
Math1 benchmarks
| Benchmark | DeepSeek V3 | Ornith-1.0-397B | Result |
|---|---|---|---|
| FrontierMath v2 (Tiers 1-3)Source | 1.724% | — | Not comparable |
Multimodal1 benchmarks
| Benchmark | DeepSeek V3 | Ornith-1.0-397B | Result |
|---|---|---|---|
| Design Arena WebsiteSource | 1152 | — | Not comparable |
Frequently Asked Questions (2)
Which is better, DeepSeek V3 or Ornith-1.0-397B?
DeepSeek V3 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, DeepSeek V3 or Ornith-1.0-397B?
Ornith-1.0-397B has the edge for coding in this comparison, averaging 74.6 versus 38.9. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
Self-host vs API cost
Estimates at 50,000 req/day · 1000 tokens/req average.
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