Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Qwen3.5-27B is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.
Confidence: limited
Qwen3.5-122B-A10B vs Qwen3.5-27B
Updated November 11, 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.
Share or export
Decision reading
Qwen3.5-122B-A10B has the higher public point estimate, 52.64 versus 48.3. Their conditional score ranges overlap. These ranges do not establish rank confidence. 18 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- API price per 1M tokens
Qwen3.5-122B-A10B: No comparable API price
Qwen3.5-27B: No comparable API price
- Context window
Qwen3.5-122B-A10B: 262K
Qwen3.5-27B: 262K
- Released
Qwen3.5-122B-A10B: March 4, 2026
Qwen3.5-27B: March 4, 2026
- Shared results
- 18
- Qwen3.5-122B-A10B only
- 0
- Qwen3.5-27B only
- 2
- Like-for-like categories
- 2 / 8
Estimated: Qwen3.5-122B-A10B and Qwen3.5-27B. Conditional ranges do not establish rank confidence.How the comparison works
Which one for your work
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.
Show secondary and unsupported calls
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Qwen3.5-122B-A10B and Qwen3.5-27B are scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
Confidence: limitedLong documents
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
Confidence: documentedChat turn cost
1K fresh input + 500 output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-ratesCache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-ratesRepository review cost
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
Which one for a specific job
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.
35.4Qwen3.5-122B-A10B34.2Qwen3.5-27B
Directional only · BenchAlign v5.8
Qwen3.5-122B-A10B has the higher coding point estimate. Conditional score ranges do not establish rank confidence.
Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.
Same basis rules as the category table below
Or describe your own job
The LLM Selector reads what you need a model for and returns a shortlist, ranked in the same order the job pages publish.
Describe the task and constraints. This selector recommends models; it cannot carry out the task or access your files and accounts.
We keep what you type here, drafts included, for 180 days, and after that only the job and requirements read from it. Nothing is tied to your IP address or account. Leave out names and other personal details. Privacy
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
18 Shared
2 Qwen3.5-27B only
5 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
Shape of the matched evidence
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.
Terminal-Bench 2.0Agentic
Normalized gap 7.8Qwen3.5-122B-A10B:49.4%Qwen3.5-27B:41.6%BrowseCompAgentic
Normalized gap 2.8Qwen3.5-122B-A10B:63.8%Qwen3.5-27B:61%CharXivMultimodal
Normalized gap 2.3Qwen3.5-122B-A10B:77.2%Qwen3.5-27B:79.5%MMMU-ProMultimodal
Normalized gap 1.9Qwen3.5-122B-A10B:76.9%Qwen3.5-27B:75%OSWorld-VerifiedAgentic
Normalized gap 1.8Qwen3.5-122B-A10B:58%Qwen3.5-27B:56.2%
Bars run 0–100 on each benchmark’s normalized display scale
Category results, on a stated basis
Each row shows the public-lane category score for both models: the BenchAlign v5.8 lane for agentic, coding, reasoning, multimodal, 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.
Reasoning
Like-for-like- Qwen3.5-122B-A10B
- 40.2
- Supported · #81/158
- Qwen3.5-27B
- 39.5
- Supported · #82/158
- Basis
- BenchAlign v5.8 lane · 1 vs 1 public rows
- Reading
- Qwen3.5-122B-A10B leads · intervals overlap
Multilingual
Like-for-like- Qwen3.5-122B-A10B
- 87.1
- #9/17
- Qwen3.5-27B
- 87.1
- #10/17
- Basis
- Provisional lane · 1 vs 1 weighted rows
- Reading
- Tie
Agentic
Directional only- Qwen3.5-122B-A10B
- 33.4
- Estimated · #95/132
- Qwen3.5-27B
- 38.9
- Estimated · #76/132
- Basis
- BenchAlign v5.8 lane · 3 vs 4 public rows
- Reading
- Directional only
Coding
Directional only- Qwen3.5-122B-A10B
- 35.4
- Supported · #87/158
- Qwen3.5-27B
- 34.2
- Estimated · #92/158
- Basis
- BenchAlign v5.8 lane · 3 vs 4 public rows
- Reading
- Directional only
Multimodal
Directional only- Qwen3.5-122B-A10B
- 51.4
- Estimated · #52/67
- Qwen3.5-27B
- 50.4
- Estimated · #55/67
- Basis
- BenchAlign v5.8 lane · 6 vs 6 public rows
- Reading
- Directional only
Knowledge
Directional only- Qwen3.5-122B-A10B
- 48.0
- Estimated · #89/179
- Qwen3.5-27B
- 44.8
- Supported · #99/179
- Basis
- BenchAlign v5.8 lane · 3 vs 3 public rows
- Reading
- Directional only
Instruction following
Directional only- Qwen3.5-122B-A10B
- 93.0
- #13/157
- Qwen3.5-27B
- 92.9
- #15/157
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Directional only
Math
Not comparable- Qwen3.5-122B-A10B
- Not ranked
- Qwen3.5-27B
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | Qwen3.5-122B-A10B | Qwen3.5-27B | Basis | Reading |
|---|---|---|---|---|
| Reasoning | 40.2Supported · #81/158 | 39.5Supported · #82/158 | Like-for-likeBenchAlign v5.8 lane · 1 vs 1 public rows | Qwen3.5-122B-A10B leads · intervals overlap |
| Multilingual | 87.1#9/17 | 87.1#10/17 | Like-for-likeProvisional lane · 1 vs 1 weighted rows | Tie |
| Agentic | 33.4Estimated · #95/132 | 38.9Estimated · #76/132 | Directional onlyBenchAlign v5.8 lane · 3 vs 4 public rows | Directional only |
| Coding | 35.4Supported · #87/158 | 34.2Estimated · #92/158 | Directional onlyBenchAlign v5.8 lane · 3 vs 4 public rows | Directional only |
| Multimodal | 51.4Estimated · #52/67 | 50.4Estimated · #55/67 | Directional onlyBenchAlign v5.8 lane · 6 vs 6 public rows | Directional only |
| Knowledge | 48.0Estimated · #89/179 | 44.8Supported · #99/179 | Directional onlyBenchAlign v5.8 lane · 3 vs 3 public rows | Directional only |
| Instruction following | 93.0#13/157 | 92.9#15/157 | Directional onlyProvisional lane · 0 vs 0 weighted rows | Directional only |
| 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, reasoning, multimodal, 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.
Supported evidence per lane · bars run 0–100Methodology
What each workload costs
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.
Chat turn
1K fresh input + 500 output tokens
- Qwen3.5-122B-A10B
- Self-hosted; infrastructure cost varies
- Fits in one request
- Qwen3.5-27B
- Self-hosted; infrastructure cost varies
- Fits in one request
Qwen3.5-122B-A10B has no comparable published API token rate. Qwen3.5-27B has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- Qwen3.5-122B-A10B
- Self-hosted; infrastructure cost varies
- Fits in one request
- Qwen3.5-27B
- Self-hosted; infrastructure cost varies
- Fits in one request
Qwen3.5-122B-A10B has no comparable published API token rate. Qwen3.5-27B has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- Qwen3.5-122B-A10B
- Self-hosted; infrastructure cost varies
- Fits in one request
- Cached-input rate unavailable
- Qwen3.5-27B
- Self-hosted; infrastructure cost varies
- Fits in one request
- Cached-input rate unavailable
Qwen3.5-122B-A10B has no comparable published API token rate. Qwen3.5-27B has no comparable published API token rate.
Cached input falls back to the list input rate only where a cached rate is unpublished
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationQwen3.5-122B-A10BQwen3.5-27B
Context window
Maximum documented context; output-token limits may be lower.
Qwen3.5-122B-A10B
262K
Qwen3.5-27B
262K
API model ID
Qwen3.5-122B-A10B
Not sourced
Qwen3.5-27B
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Qwen3.5-122B-A10B
No comparable hosted API rate
Qwen3.5-27B
No comparable hosted API rate
Documented inputs
Qwen3.5-122B-A10B
text, image, video
Qwen3.5-122B-A10B model cardQwen3.5-27B
text, image, video
Qwen3.5-27B model cardDocumented outputs
Qwen3.5-122B-A10B
Qwen3.5-27B
Provider availability
Qwen3.5-122B-A10B
Not sourced
Qwen3.5-27B
Not sourced
Reasoning profile
Qwen3.5-122B-A10B
Reasoning
Qwen3.5-27B
Reasoning
Weight access
Qwen3.5-122B-A10B
Open Weight
Qwen3.5-27B
Open Weight
License
Qwen3.5-122B-A10B
Open Weight
Qwen3.5-27B
Open Weight
Release date
Qwen3.5-122B-A10B
2026-03-04
Qwen3.5-27B
2026-03-04
If you already use one of these models
- Deployment change
- Both entries list Alibaba as the provider. Confirm endpoint, model ID, limits, and feature support before switching.
- Quality signal
- Qwen3.5-122B-A10B has the higher public point estimate, 52.64 versus 48.3. Their conditional score ranges overlap. These ranges do not establish rank confidence.
- Workload cost
- A complete comparable API-rate estimate is not available for both models.
- Context tradeoff
- Both models list 262K.
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, Qwen3.5-122B-A10B or Qwen3.5-27B?
Qwen3.5-122B-A10B has the higher public point estimate, 52.64 versus 48.3. Their conditional score ranges overlap. These ranges do not establish rank confidence. The page therefore keeps the decision tied to the specific documented workload.
Which is better for coding, Qwen3.5-122B-A10B or Qwen3.5-27B?
Qwen3.5-122B-A10B scores higher for coding on the public lane, 35.4 to 34.2. Qwen3.5-27B is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like. BenchLM does not name a winner for coding from a directional reading; read the shared benchmark rows directly and test the models on the same task set.
Which is better for agentic tasks, Qwen3.5-122B-A10B or Qwen3.5-27B?
Qwen3.5-27B scores higher for agentic tasks on the public lane, 38.9 to 33.4. Qwen3.5-122B-A10B and Qwen3.5-27B 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.
Which costs less, Qwen3.5-122B-A10B or Qwen3.5-27B?
Both models do not have comparable published API token rates, so this page does not name a universal price winner.
Which has the larger context window, Qwen3.5-122B-A10B or Qwen3.5-27B?
Both models list the same context window, 262K.
Self-host vs API cost
Estimates at 50,000 req/day · 1000 tokens/req average.
Qwen3.5-122B-A10B
API / mo (Novita AI)$2,700
Self-host / mo$3,495
Break-even65M/day
Qwen3.5-27B
API / moNo API price on file
Self-host / moNot listed
Break-even—
Proprietary model — self-hosting not applicable.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence20 rows
Agentic
Terminal-Bench 2.0
Qwen3.5-122B-A10B49.4%SourceQwen3.5-27B41.6%SourceQwen3.5-122B-A10B leads this result
BrowseComp
Qwen3.5-122B-A10B63.8%SourceQwen3.5-27B61%SourceQwen3.5-122B-A10B leads this result
OSWorld-Verified
Qwen3.5-122B-A10B58%SourceQwen3.5-27B56.2%SourceQwen3.5-122B-A10B leads this result
Gert Labs
Qwen3.5-122B-A10B—Qwen3.5-27B39.41%SourceNot directly comparable
Coding
SWE-bench Verified
Qwen3.5-122B-A10B72%SourceQwen3.5-27B72.4%SourceQwen3.5-27B leads this result
LiveCodeBench v6
Qwen3.5-122B-A10B78.9%SourceQwen3.5-27B80.7%SourceQwen3.5-27B leads this result
Terminal-Bench 2.0
Qwen3.5-122B-A10B49.4%SourceQwen3.5-27B41.6%SourceQwen3.5-122B-A10B leads this result
SWE-Rebench
Qwen3.5-122B-A10B—Qwen3.5-27B58.9%SourceNot directly comparable
Reasoning
Multimodal
MMMU
Shared sourceQwen3.5-122B-A10B83.9%Qwen3.5-27B82.3%Qwen3.5-122B-A10B leads this result
MMVU
Shared sourceQwen3.5-122B-A10B74.7%Qwen3.5-27B73.3%Qwen3.5-122B-A10B leads this result
MMMU-Pro
Qwen3.5-122B-A10B76.9%SourceQwen3.5-27B75%SourceQwen3.5-122B-A10B leads this result
MathVision
Shared sourceQwen3.5-122B-A10B86.2%Qwen3.5-27B86.0%Qwen3.5-122B-A10B leads this result
CharXiv
Qwen3.5-122B-A10B77.2%SourceQwen3.5-27B79.5%SourceQwen3.5-27B leads this result
- Qwen3.5-122B-A10B93.2%Qwen3.5-27B93.7%
Qwen3.5-27B leads this result
Knowledge
Multilingual
Instruction following
20 public results · 18 shared
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Last updated November 11, 2026