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Qwen3.5-35B-A3B-Base vs Gemma 4 31B

Benchmark Performance

Available Benchmarks

All benchmark results →
No Protocol-Matched Benchmark Yet.Results appear here only when both models share the same benchmark version, metric, evaluation protocol, and evidence class.
FieldAt a Glance
Qwen · activeQwen3.5-35B-A3B-BaseVerified Aug 28, 2026
Google Deepmind · activeGemma 4 31BVerified Sep 3, 2026
Not a Valid ComparisonThese records do not share a sourced modality and workload.

Technical Differences

Side-by-Side Facts

Interactive only
FieldQwen3.5-35B-A3B-BaseGemma 4 31B
DeveloperQwenGoogle Deepmind
FamilyQwen3 5 35b A3b BaseGemma 4
ModelQwen3.5-35B-A3B-BaseGemma 4 31B
VersionQwen3.5-35B-A3B-BaseGemma 4 31B
Lifecycleactiveactive
ReleasedUnknown2026-03-11
Knowledge cutoffUnknownUnknown
Input modalitiesText, ImageUnknown
Output modalitiesTextUnknown
Context window262,144262,144
Total parameters35,951,822,70431,000,000,000
Active parameters3,000,000,000Unknown
Licenseapache-2.0apache-2.0
Open weightsYesYes
API availableUnknownYes
Self-hostableYesYes
Provider accessUnknownCerebras (Standard), Deepinfra (Standard), Google Gemini (Standard), Openrouter (Standard), Together Ai (Standard)
Capabilitieschat, generation, toolschat, generation, reasoning, structured_outputs, tools

11 comparable fields · 6 material differences · Interactive comparison only; indexing gate not met

Qwen3.5-35B-A3B-Base Capabilities

chatgenerationtools
Input price—
Output price—
Serving providers0
Canonical IDQwen/Qwen3.5-35B-A3B-Base

Gemma 4 31B Capabilities

chatgenerationreasoningstructured outputstools
Input price$0.090
Output price$0.34
Serving providers5
Canonical IDgoogle/gemma-4-31B-it

Internal Comparison Graph

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Primary Evidence

Sources and Freshness

Qwen/Qwen3.5-35B-A3B-Base developer model repositoryQwen · observed Aug 28, 2026google/gemma-4-31B-it developer model repositoryGoogle DeepMind · observed Sep 3, 2026

Questions

Qwen3.5-35B-A3B-Base vs Gemma 4 31B FAQs

Is Qwen3.5-35B-A3B-Base or Gemma 4 31B better for coding?+

This comparison does not currently contain a protocol-matched coding benchmark for both Qwen3.5-35B-A3B-Base and Gemma 4 31B, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.

Which is cheaper, Qwen3.5-35B-A3B-Base or Gemma 4 31B?+

Only Gemma 4 31B has a directly sourced input price: $0.090 per million tokens. Only Gemma 4 31B has a directly sourced output price: $0.34 per million tokens.

Which has a larger context window, Qwen3.5-35B-A3B-Base or Gemma 4 31B?+

Neither model has a larger sourced context window in this comparison. Qwen3.5-35B-A3B-Base is 262,144 and Gemma 4 31B is 262,144.

Which performs better in benchmarks, Qwen3.5-35B-A3B-Base or Gemma 4 31B?+

There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.

Can Qwen3.5-35B-A3B-Base or Gemma 4 31B be self-hosted?+

Both models have the same recorded self-hosting status: supported. Qwen3.5-35B-A3B-Base is open weight; Gemma 4 31B is open weight.

Can Qwen3.5-35B-A3B-Base and Gemma 4 31B understand images?+

Qwen3.5-35B-A3B-Base is documented with image input; Gemma 4 31B is not documented with image input. This reflects supported input modalities, not vision quality.

Which can generate longer answers, Qwen3.5-35B-A3B-Base or Gemma 4 31B?+

Neither has a larger sourced maximum output. Qwen3.5-35B-A3B-Base is — and Gemma 4 31B is —.

Do Qwen3.5-35B-A3B-Base and Gemma 4 31B support reasoning and tool use?+

Qwen3.5-35B-A3B-Base: tool calling and image input. Gemma 4 31B: reasoning and tool calling. Feature support does not establish relative quality.

Which is available from more inference providers, Qwen3.5-35B-A3B-Base or Gemma 4 31B?+

Qwen3.5-35B-A3B-Base has 0 sourced provider routes; Gemma 4 31B has 5, so Gemma 4 31B has broader tracked availability.

Which offers better value, Qwen3.5-35B-A3B-Base or Gemma 4 31B?+

There is no universal value winner. Compare the input and output prices above with the matched benchmark result for your workload: cheaper tokens can be offset by different quality, token usage, latency, or provider availability.

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