Long documents
Prompts that approach the documented context limit
Qwen3.5-35B-A3B
Qwen3.5-35B-A3B has the larger documented context window.
Confidence: documented
Gemma 4 31B vs Qwen3.5-35B-A3B
Updated November 12, 2026. Rank says Qwen3.5-35B-A3B is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
Share or export
Decision reading
Qwen3.5-35B-A3B has the higher public point estimate, 45.25 versus 28.83. Their conditional score ranges overlap. These ranges do not establish rank confidence. 6 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
Gemma 4 31B: No comparable API price
Qwen3.5-35B-A3B: No comparable API price
- Context window
Gemma 4 31B: 256K
Qwen3.5-35B-A3B: 262K
- Released
Gemma 4 31B: April 2, 2026
Qwen3.5-35B-A3B: March 4, 2026
- Shared results
- 6
- Gemma 4 31B only
- 7
- Qwen3.5-35B-A3B only
- 15
- Like-for-like categories
- 2 / 8
Estimated: Gemma 4 31B · Supported: Qwen3.5-35B-A3B. 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.
Show secondary and unsupported calls
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Qwen3.5-35B-A3B is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.
Confidence: limitedAgentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Gemma 4 31B and Qwen3.5-35B-A3B are scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
Confidence: limitedChat 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.
28.8Gemma 4 31B27.0Qwen3.5-35B-A3B
Directional only · BenchAlign v5.8
Gemma 4 31B 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.
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What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
7 Gemma 4 31B only
6 Shared
15 Qwen3.5-35B-A3B only
4 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.
SWE-RebenchCoding
Normalized gap 12.1Gemma 4 31B:41.6%Qwen3.5-35B-A3B:53.7%MMMU-ProMultimodal
Normalized gap 1.8Gemma 4 31B:76.9%Qwen3.5-35B-A3B:75.1%GPQAKnowledge
Normalized gap 0.1Gemma 4 31B:84.3%Qwen3.5-35B-A3B:84.2%MMLU-ProKnowledge
Normalized gap 0.1Gemma 4 31B:85.2%Qwen3.5-35B-A3B:85.3%
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- Gemma 4 31B
- 32.0
- Supported · #109/160
- Qwen3.5-35B-A3B
- 36.8
- Supported · #87/160
- Basis
- BenchAlign v5.8 lane · 1 vs 1 public rows
- Reading
- Qwen3.5-35B-A3B leads · intervals overlap
Knowledge
Like-for-like- Gemma 4 31B
- 36.7
- Supported · #121/180
- Qwen3.5-35B-A3B
- 42.4
- Supported · #105/180
- Basis
- BenchAlign v5.8 lane · 4 vs 3 public rows
- Reading
- Qwen3.5-35B-A3B leads · intervals overlap
Agentic
Directional only- Gemma 4 31B
- 12.5
- Estimated · #133/135
- Qwen3.5-35B-A3B
- 33.1
- Estimated · #97/135
- Basis
- BenchAlign v5.8 lane · 3 vs 4 public rows
- Reading
- Directional only
Coding
Directional only- Gemma 4 31B
- 28.8
- Supported · #107/162
- Qwen3.5-35B-A3B
- 27.0
- Estimated · #120/162
- Basis
- BenchAlign v5.8 lane · 3 vs 4 public rows
- Reading
- Directional only
Multimodal
Directional only- Gemma 4 31B
- 47.3
- Supported · #60/67
- Qwen3.5-35B-A3B
- 47.7
- Estimated · #61/67
- Basis
- BenchAlign v5.8 lane · 2 vs 7 public rows
- Reading
- Directional only
Instruction following
Directional only- Gemma 4 31B
- 92.9
- #16/157
- Qwen3.5-35B-A3B
- 88.8
- #36/157
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Directional only
Multilingual
Not comparable- Gemma 4 31B
- Not ranked
- Qwen3.5-35B-A3B
- 83.8
- #11/17
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Not comparable
Math
Not comparable- Gemma 4 31B
- Not ranked
- Qwen3.5-35B-A3B
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | Gemma 4 31B | Qwen3.5-35B-A3B | Basis | Reading |
|---|---|---|---|---|
| Reasoning | 32.0Supported · #109/160 | 36.8Supported · #87/160 | Like-for-likeBenchAlign v5.8 lane · 1 vs 1 public rows | Qwen3.5-35B-A3B leads · intervals overlap |
| Knowledge | 36.7Supported · #121/180 | 42.4Supported · #105/180 | Like-for-likeBenchAlign v5.8 lane · 4 vs 3 public rows | Qwen3.5-35B-A3B leads · intervals overlap |
| Agentic | 12.5Estimated · #133/135 | 33.1Estimated · #97/135 | Directional onlyBenchAlign v5.8 lane · 3 vs 4 public rows | Directional only |
| Coding | 28.8Supported · #107/162 | 27.0Estimated · #120/162 | Directional onlyBenchAlign v5.8 lane · 3 vs 4 public rows | Directional only |
| Multimodal | 47.3Supported · #60/67 | 47.7Estimated · #61/67 | Directional onlyBenchAlign v5.8 lane · 2 vs 7 public rows | Directional only |
| Instruction following | 92.9#16/157 | 88.8#36/157 | Directional onlyProvisional lane · 0 vs 0 weighted rows | Directional only |
| Multilingual | Not ranked | 83.8#11/17 | Not comparableProvisional lane · 0 vs 1 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, 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
- Gemma 4 31B
- Self-hosted; infrastructure cost varies
- Fits in one request
- Qwen3.5-35B-A3B
- Self-hosted; infrastructure cost varies
- Fits in one request
Gemma 4 31B has no comparable published API token rate. Qwen3.5-35B-A3B has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- Gemma 4 31B
- Self-hosted; infrastructure cost varies
- Fits in one request
- Qwen3.5-35B-A3B
- Self-hosted; infrastructure cost varies
- Fits in one request
Gemma 4 31B has no comparable published API token rate. Qwen3.5-35B-A3B has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- Gemma 4 31B
- Self-hosted; infrastructure cost varies
- Fits in one request
- Cached-input rate unavailable
- Qwen3.5-35B-A3B
- Self-hosted; infrastructure cost varies
- Fits in one request
- Cached-input rate unavailable
Gemma 4 31B has no comparable published API token rate. Qwen3.5-35B-A3B 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.
SpecificationGemma 4 31BQwen3.5-35B-A3B
Context window
Maximum documented context; output-token limits may be lower.
Gemma 4 31B
Qwen3.5-35B-A3B
262K
API model ID
Gemma 4 31B
gemma-4-31b-it
Google Gemma Gemini API guideQwen3.5-35B-A3B
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Gemma 4 31B
No comparable hosted API rate
Qwen3.5-35B-A3B
No comparable hosted API rate
Documented inputs
Gemma 4 31B
text, image
Google Gemma 4 model documentationQwen3.5-35B-A3B
text, image, video
Qwen3.5-35B-A3B model cardDocumented outputs
Gemma 4 31B
Qwen3.5-35B-A3B
Provider availability
Gemma 4 31B
Generally Available · Gemini API, Google AI Studio, open weights
Google Gemma Gemini API guideQwen3.5-35B-A3B
Not sourced
Reasoning profile
Gemma 4 31B
Reasoning
Qwen3.5-35B-A3B
Reasoning
Weight access
Gemma 4 31B
Open Weight
Qwen3.5-35B-A3B
Open Weight
License
Gemma 4 31B
Open Weight
Qwen3.5-35B-A3B
Open Weight
Release date
Gemma 4 31B
2026-04-02
Qwen3.5-35B-A3B
2026-03-04
If you already use one of these models
- Deployment change
- The models list different providers, so authentication, endpoint behavior, limits, and feature support may change.
- Quality signal
- Qwen3.5-35B-A3B has the higher public point estimate, 45.25 versus 28.83. 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
- Qwen3.5-35B-A3B has the larger documented window (262K).
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, Gemma 4 31B or Qwen3.5-35B-A3B?
Qwen3.5-35B-A3B has the higher public point estimate, 45.25 versus 28.83. 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, Gemma 4 31B or Qwen3.5-35B-A3B?
Gemma 4 31B scores higher for coding on the public lane, 28.8 to 27. Qwen3.5-35B-A3B 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, Gemma 4 31B or Qwen3.5-35B-A3B?
Qwen3.5-35B-A3B scores higher for agentic tasks on the public lane, 33.1 to 12.5. Gemma 4 31B and Qwen3.5-35B-A3B 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, Gemma 4 31B or Qwen3.5-35B-A3B?
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, Gemma 4 31B or Qwen3.5-35B-A3B?
Qwen3.5-35B-A3B has the larger documented context window: 262K, compared with 256K.
Self-host vs API cost
Estimates at 50,000 req/day · 1000 tokens/req average.
Gemma 4 31B
API / mo (Novita AI)$405
Self-host / mo$298
Break-even37M/day
Qwen3.5-35B-A3B
API / mo (DeepInfra)$855
Self-host / mo$298
Break-even17M/day
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence28 rows
Agentic
b3 Agent Security
Gemma 4 31B83.0%SourceQwen3.5-35B-A3B—Not directly comparable
Cyber Misuse Refusal
Gemma 4 31B84.0%SourceQwen3.5-35B-A3B—Not directly comparable
Gert Labs
Shared sourceGemma 4 31B35.26%Qwen3.5-35B-A3B28.96%Gemma 4 31B leads this result
Terminal-Bench 2.0
Gemma 4 31B—Qwen3.5-35B-A3B40.5%SourceNot directly comparable
BrowseComp
Gemma 4 31B—Qwen3.5-35B-A3B61%SourceNot directly comparable
OSWorld-Verified
Gemma 4 31B—Qwen3.5-35B-A3B54.5%SourceNot directly comparable
Coding
LiveCodeBench v6
Gemma 4 31B80.0%SourceQwen3.5-35B-A3B74.6%SourceGemma 4 31B leads this result
SWE-Rebench
Shared sourceGemma 4 31B41.6%Qwen3.5-35B-A3B53.7%Qwen3.5-35B-A3B leads this result
React Native Evals
Gemma 4 31B75.2%SourceQwen3.5-35B-A3B—Not directly comparable
SWE-bench Verified
Gemma 4 31B—Qwen3.5-35B-A3B69.2%SourceNot directly comparable
Terminal-Bench 2.0
Gemma 4 31B—Qwen3.5-35B-A3B40.5%SourceNot directly comparable
Reasoning
Multimodal
ParseBench
Gemma 4 31B62.40%SourceQwen3.5-35B-A3B—Not directly comparable
MMMU-Pro
Gemma 4 31B76.9%SourceQwen3.5-35B-A3B75.1%SourceGemma 4 31B leads this result
ExtractBench (LlamaIndex)
Gemma 4 31B—Qwen3.5-35B-A3B88.00%SourceNot directly comparable
MMMU
Gemma 4 31B—Qwen3.5-35B-A3B81.4%SourceNot directly comparable
MMVU
Gemma 4 31B—Qwen3.5-35B-A3B72.3%SourceNot directly comparable
CharXiv
Gemma 4 31B—Qwen3.5-35B-A3B77.5%SourceNot directly comparable
MathVision
Gemma 4 31B—Qwen3.5-35B-A3B83.9%SourceNot directly comparable
V*
Gemma 4 31B—Qwen3.5-35B-A3B92.7%SourceNot directly comparable
Knowledge
GPQA
Gemma 4 31B84.3%SourceQwen3.5-35B-A3B84.2%SourceGemma 4 31B leads this result
MMLU-Pro
Gemma 4 31B85.2%SourceQwen3.5-35B-A3B85.3%SourceQwen3.5-35B-A3B leads this result
HLE
Gemma 4 31B26.5%SourceQwen3.5-35B-A3B—Not directly comparable
HLE w/o tools
Gemma 4 31B19.5%SourceQwen3.5-35B-A3B—Not directly comparable
SuperGPQA
Gemma 4 31B—Qwen3.5-35B-A3B63.4%SourceNot directly comparable
Multilingual
MMLU-ProX
Gemma 4 31B—Qwen3.5-35B-A3B81%SourceNot directly comparable
Instruction following
IFEval
Gemma 4 31B—Qwen3.5-35B-A3B91.9%SourceNot directly comparable
28 public results · 6 shared
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Last updated November 12, 2026