Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Ornith-1.5-9B is not ranked on the public lane for coding, so no winner is named for coding.
Updated October 2, 2026. Rank says Qwen3.5-122B-A10B is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
Both of these models will change. Get the price, version and retirement notices for the pair, each with its source. Follow model changes
Qwen3.5-122B-A10B has the higher public point estimate, 41.66 versus 28.63. Their conditional score ranges overlap. These ranges do not establish rank confidence. 3 results are shared. Category rows resting on Estimated evidence or 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.
No workload recommendation clears the current evidence threshold.
Use the matched evidence, workload costs, and sourced specifications below instead of treating a point score as a universal answer.
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Ornith-1.5-9B is not ranked on the public lane for coding, so no winner is named for coding.
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Ornith-1.5-9B and Qwen3.5-122B-A10B are scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
1K fresh input + 500 output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
200K cached + 20K fresh input + 10K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
50K fresh input + 3K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Choose a job from the LLM Selector's task catalog to see the category row it rests on, under the same basis rules as the table below. A directional row stays directional; choosing a job never creates a winner.
The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.
Not comparable · BenchAlign v5.8
The coding row is not comparable on the public lane: at least one model is not measured or not ranked there.
Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
2 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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.
BrowseCompAgentic
Normalized gap 7.4SWE-bench VerifiedCoding
Normalized gap 1.4GPQAKnowledge
Normalized gap 0.2Each row shows the public-lane category score for both models: the BenchAlign v5.8 lane for agentic, coding, and knowledge, and the weighted public rows elsewhere, with the same rank each model page reports. A row is like-for-like only when both scores rest on Supported evidence or the same weighted set. Directional and not-comparable rows remain visible, but they never receive a winner.
| Category | Ornith-1.5-9B | Qwen3.5-122B-A10B | Basis | Reading |
|---|---|---|---|---|
| Agentic | 18.0Estimated · #101/119 | 22.6Estimated · #96/119 | Directional onlyBenchAlign v5.8 lane · 7 vs 3 public rows | Directional only |
| Knowledge | 30.6Estimated · #134/171 | 42.3Supported · #93/171 | Directional onlyBenchAlign v5.8 lane · 4 vs 3 public rows | Directional only |
| Coding | Not ranked | 35.4Supported · #81/144 | Not comparableBenchAlign v5.8 lane · 5 vs 1 public rows | Not comparable |
| Reasoning | Not ranked | 49.9Unranked · 3 rankable rows | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
| Multimodal | Not ranked | 58.0#33/49 | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
| Multilingual | Not ranked | 87.1#9/16 | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
| Instruction following | Not ranked | 91.6#11/125 | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.8) differ from the provisional-lane categories. Unranked scores sit on the provisional lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.
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. Qwen3.5-122B-A10B has no comparable published API token rate.
50K fresh input + 3K output tokens
Ornith-1.5-9B has no comparable published API token rate. Qwen3.5-122B-A10B has no comparable published API token rate.
200K cached + 20K fresh input + 10K output tokens
Ornith-1.5-9B has no comparable published API token rate. Qwen3.5-122B-A10B 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.
Ornith-1.5-9B
Qwen3.5-122B-A10B
262K
Ornith-1.5-9B
Not sourced
Qwen3.5-122B-A10B
Not sourced
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Ornith-1.5-9B
No comparable hosted API rate
Ornith-1.5-9B model cardQwen3.5-122B-A10B
No comparable hosted API rate
Ornith-1.5-9B
Not sourced
Qwen3.5-122B-A10B
Not sourced
Ornith-1.5-9B
Not sourced
Qwen3.5-122B-A10B
Not sourced
Ornith-1.5-9B
Not sourced
Qwen3.5-122B-A10B
Not sourced
Ornith-1.5-9B
Reasoning
Qwen3.5-122B-A10B
Reasoning
Ornith-1.5-9B
Open Weight
Qwen3.5-122B-A10B
Open Weight
Ornith-1.5-9B
Open Weight
Qwen3.5-122B-A10B
Open Weight
Ornith-1.5-9B
2026-08-18
Qwen3.5-122B-A10B
2026-03-04
Qwen3.5-122B-A10B has the higher public point estimate, 41.66 versus 28.63. Their conditional score ranges overlap. These ranges do not establish rank confidence. The page therefore keeps the decision tied to the specific documented workload.
Ornith-1.5-9B is not ranked on the public lane for coding, so no winner is named for coding.
Qwen3.5-122B-A10B scores higher for agentic tasks on the public lane, 22.6 to 18. Ornith-1.5-9B and Qwen3.5-122B-A10B are scored on Estimated evidence for agentic tasks, so the reading is directional rather than like-for-like. BenchLM does not name a winner for agentic tasks from a directional reading; read the shared benchmark rows directly and test the models on the same task set.
Both models do not have comparable published API token rates, so this page does not name a universal price winner.
Both models list the same context window, 262K.
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
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
Qwen3.5-122B-A10B leads this result
Claw-Eval
Not directly comparable
Terminal-Bench 2.0
Not directly comparable
OSWorld-Verified
Not directly comparable
Terminal-Bench 2.1
Not directly comparable
SWE-bench Verified
Qwen3.5-122B-A10B leads this result
SWE-bench Pro
Not directly comparable
SWE Multilingual
Not directly comparable
NL2Repo
Not directly comparable
LongBench v2
Not directly comparable
MMMU
Not directly comparable
MMVU
Not directly comparable
MathVision
Not directly comparable
CharXiv
Not directly comparable
V*
Not directly comparable
GPQA
Qwen3.5-122B-A10B leads this result
GPQA-D
Not directly comparable
HLE
Not directly comparable
HLE w/o tools
Not directly comparable
MMLU-Pro
Not directly comparable
SuperGPQA
Not directly comparable
MMLU-ProX
Not directly comparable
IFEval
Not directly comparable
One weekly email when material rank, price, or benchmark evidence changes make this matchup worth revisiting.
Read a sample issueJoin 2,000+ readers.
Last updated October 2, 2026