Coding
Directional only- Claude 4 Sonnet
- 72.7
- Ornith-1.5-9B
- 61.7
- Weighted basis
- 1 vs 2 rows
- Reading
- Directional only
Five or fewer confirmed AI changes, with original sources, on mornings when something changed.A free source-linked morning brief.
Start free briefUpdated August 19, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
Claude 4 Sonnet has the higher public score estimate, 41.98 versus 36.95, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
1 results are shared. Category rows based on different benchmark sets are marked directional and do not name a winner.
Recommendations appear only when a shared evidence basis or an explicit operating constraint supports the call. Secondary and unsupported use cases stay disclosed below the initial list.
Prompts that approach the documented context limit
Ornith-1.5-9B
Ornith-1.5-9B has the larger documented context window.
Confidence: documented
Code generation, repair, and software-engineering tasks
Not enough matched evidence
The category averages use different weighted benchmark sets, so they are directional rather than like-for-like.
Confidence: limited
Tool use, computer use, and multi-step task completion
Not enough matched evidence
No shared weighted benchmark basis supports a winner.
Confidence: limited
1K fresh input + 500 output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
200K cached + 20K fresh input + 10K output tokens
Not enough matched evidence
The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. Claude 4 Sonnet does not fit this workload in one request. Claude 4 Sonnet has no published cached-input rate, so cached tokens use its listed input rate. Ornith-1.5-9B has no comparable published API token rate.
Confidence: rate-fallback
50K fresh input + 3K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
1 category uses different evidence sets. Those rows remain visible for coverage context but do not name a winner.
Each row states whether both averages use the same weighted benchmark set. Directional and not-comparable rows remain visible, but they never receive a winner in this template.
| Category | Claude 4 Sonnet | Ornith-1.5-9B | Weighted basis | Reading |
|---|---|---|---|---|
| Coding | 72.7 | 61.7 | Directional only1 vs 2 rows | Directional only |
| Agentic | Not measured | 56.4 | Not comparable0 vs 1 rows | Not comparable |
| Reasoning | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Knowledge | Not measured | 29.1 | Not comparable0 vs 2 rows | Not comparable |
| Math | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Multilingual | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Multimodal | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Instruction following | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
Only shared public evidence is shown. Sparse evidence stays a ruled list rather than being closed into a radar shape.
Too few matched category axes support a radar. The ruled list below shows only shared benchmark results; positions use each benchmark’s normalized display scale when available.
SWE-bench Verified
Coding
Three fixed token mixes turn per-token rates into comparable decisions. Each scenario states context fit and whether cached input had to fall back to the published list-input rate.
1K fresh input + 500 output tokens
Ornith-1.5-9B has no comparable published API token rate.
50K fresh input + 3K output tokens
Ornith-1.5-9B has no comparable published API token rate.
200K cached + 20K fresh input + 10K output tokens
Claude 4 Sonnet does not fit this workload in one request. Claude 4 Sonnet has no published cached-input rate, so cached tokens use its listed input rate. Ornith-1.5-9B has no comparable published API token rate.
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
Maximum documented context; output-token limits may be lower.
Claude 4 Sonnet
200K
Ornith-1.5-9B
Claude 4 Sonnet
Not sourced
Ornith-1.5-9B
Not sourced
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Claude 4 Sonnet
Not published
Ornith-1.5-9B
No comparable hosted API rate
Ornith-1.5-9B model cardClaude 4 Sonnet
Not sourced
Ornith-1.5-9B
Not sourced
Claude 4 Sonnet
Not sourced
Ornith-1.5-9B
Not sourced
Claude 4 Sonnet
Not sourced
Ornith-1.5-9B
Not sourced
Claude 4 Sonnet
Non-Reasoning
Ornith-1.5-9B
Reasoning
Claude 4 Sonnet
Proprietary
Ornith-1.5-9B
Open Weight
Claude 4 Sonnet
Proprietary
Ornith-1.5-9B
Open Weight
Claude 4 Sonnet
2025-05-01
Ornith-1.5-9B
2026-08-18
Run the same representative tasks against both endpoints before changing production traffic.
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Gert Labs
Not directly comparable
JobBench
Not directly comparable
Terminal-Bench 2.1
Not directly comparable
HLE w/ tools
Not directly comparable
MCP Atlas
Not directly comparable
Toolathlon-Verified
Not directly comparable
WideResearch
Not directly comparable
BrowseComp
Not directly comparable
Claw-Eval
Not directly comparable
SWE-bench Verified
Claude 4 Sonnet leads this result
Terminal-Bench 2.1
Not directly comparable
SWE-bench Pro
Not directly comparable
SWE Multilingual
Not directly comparable
NL2Repo
Not directly comparable
Claude 4 Sonnet has the higher public score estimate, 41.98 versus 36.95, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
The current coding averages use different weighted benchmark sets, so BenchLM does not name a winner from them. Read the shared benchmark rows directly and test the models on the same task set.
The published evidence does not provide a shared weighted agentic tasks basis for both models, so BenchLM does not name a agentic tasks winner.
Both models do not have comparable published API token rates, so this page does not name a universal price winner.
Ornith-1.5-9B has the larger documented context window: 262K, compared with 200K.
Last updated August 19, 2026
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