Gemma 4 26B A4B vs Qwen3.6 Plus

Updated November 13, 2026. Rank says Qwen3.6 Plus is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

Decision reading

Qwen3.6 Plus has the higher public point estimate, 53.41 versus 41.23. Their conditional score ranges overlap. These ranges do not establish rank confidence. 5 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A

Gemma 4 26B A4B

Google

41.23/100

Estimated · Public rank #132

Conditional range 31.5–51.0

Model B

Qwen3.6 Plus

Alibaba

53.41/100

Supported · Public rank #76

90% interval 43.5–63.3

API price per 1M tokens

Gemma 4 26B A4B: No comparable API price

Qwen3.6 Plus: No comparable API price

Context window

Gemma 4 26B A4B: 256K

Qwen3.6 Plus: 1M

Released

Gemma 4 26B A4B: April 2, 2026

Qwen3.6 Plus: April 2, 2026

Shared results: 5
Gemma 4 26B A4B only: 5
Qwen3.6 Plus only: 44
Like-for-like categories: 2 / 8

Estimated: Gemma 4 26B A4B · Supported: Qwen3.6 Plus. 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.

Coding work
Code generation, repair, and software-engineering tasks
Qwen3.6 Plus
Qwen3.6 Plus has the higher public coding point estimate, 39.8 to 30.6, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Confidence: limited
Long documents
Prompts that approach the documented context limit
Qwen3.6 Plus
Qwen3.6 Plus has the larger documented context window.
Confidence: documented

Show secondary and unsupported calls

Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Gemma 4 26B A4B is not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limited
Chat 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-rates
Cache-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-rates
Repository 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.

Pick the job you would use them for

The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.

30.6Gemma 4 26B A4B39.8Qwen3.6 Plus

Like-for-like · BenchAlign v5.8

Qwen3.6 Plus 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.

Open the coding leaderboard Shortlist models for this job

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.

What do you need a model for?

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

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.

HLEKnowledge
Normalized gap 11.6
Gemma 4 26B A4B:17.2%
Qwen3.6 Plus:28.8%
Gemma 4 26B A4B source Qwen3.6 Plus source
GPQAKnowledge
Normalized gap 8.1
Gemma 4 26B A4B:82.3%
Qwen3.6 Plus:90.4%
Gemma 4 26B A4B source Qwen3.6 Plus source
MMLU-ProKnowledge
Normalized gap 5.9
Gemma 4 26B A4B:82.6%
Qwen3.6 Plus:88.5%
Gemma 4 26B A4B source Qwen3.6 Plus source
MMMU-ProMultimodal
Normalized gap 5.0
Gemma 4 26B A4B:73.8%
Qwen3.6 Plus:78.8%
Gemma 4 26B A4B source Qwen3.6 Plus source

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.

Coding

Like-for-like

Gemma 4 26B A4B: 30.6; Supported · #103/162
Qwen3.6 Plus: 39.8; Supported · #72/162
Basis: BenchAlign v5.8 lane · 2 vs 9 public rows
Reading: Qwen3.6 Plus leads · intervals overlap

Reasoning

Like-for-like

Gemma 4 26B A4B: 35.0; Supported · #102/160
Qwen3.6 Plus: 35.6; Supported · #98/160
Basis: BenchAlign v5.8 lane · 1 vs 2 public rows
Reading: Qwen3.6 Plus leads · intervals overlap

Multimodal

Directional only

Gemma 4 26B A4B: 48.6; Supported · #56/67
Qwen3.6 Plus: 55.2; Estimated · #46/67
Basis: BenchAlign v5.8 lane · 3 vs 7 public rows
Reading: Directional only

Knowledge

Directional only

Gemma 4 26B A4B: 41.4; Estimated · #116/180
Qwen3.6 Plus: 53.1; Supported · #66/180
Basis: BenchAlign v5.8 lane · 4 vs 8 public rows
Reading: Directional only

Instruction following

Directional only

Gemma 4 26B A4B: 88.7; #37/157
Qwen3.6 Plus: 85.8; #50/157
Basis: Provisional lane · 0 vs 1 weighted rows
Reading: Directional only

Agentic

Not comparable

Gemma 4 26B A4B: Not ranked
Qwen3.6 Plus: 34.3; Supported · #87/135
Basis: BenchAlign v5.8 lane · 0 vs 13 public rows
Reading: Not comparable

Multilingual

Not comparable

Gemma 4 26B A4B: Not ranked
Qwen3.6 Plus: 93.8; #4/17
Basis: Provisional lane · 0 vs 1 weighted rows
Reading: Not comparable

Math

Not comparable

Gemma 4 26B A4B: Not ranked
Qwen3.6 Plus: 62.3; #6/12
Basis: Provisional lane · 0 vs 3 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for Gemma 4 26B A4B and Qwen3.6 Plus
Category	Gemma 4 26B A4B	Qwen3.6 Plus	Basis	Reading
Coding	30.6Supported · #103/162	39.8Supported · #72/162	Like-for-likeBenchAlign v5.8 lane · 2 vs 9 public rows	Qwen3.6 Plus leads · intervals overlap
Reasoning	35.0Supported · #102/160	35.6Supported · #98/160	Like-for-likeBenchAlign v5.8 lane · 1 vs 2 public rows	Qwen3.6 Plus leads · intervals overlap
Multimodal	48.6Supported · #56/67	55.2Estimated · #46/67	Directional onlyBenchAlign v5.8 lane · 3 vs 7 public rows	Directional only
Knowledge	41.4Estimated · #116/180	53.1Supported · #66/180	Directional onlyBenchAlign v5.8 lane · 4 vs 8 public rows	Directional only
Instruction following	88.7#37/157	85.8#50/157	Directional onlyProvisional lane · 0 vs 1 weighted rows	Directional only
Agentic	Not ranked	34.3Supported · #87/135	Not comparableBenchAlign v5.8 lane · 0 vs 13 public rows	Not comparable
Multilingual	Not ranked	93.8#4/17	Not comparableProvisional lane · 0 vs 1 weighted rows	Not comparable
Math	Not ranked	62.3#6/12	Not comparableProvisional lane · 0 vs 3 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 26B A4B: Self-hosted; infrastructure cost varies; Fits in one request
Qwen3.6 Plus: API rate not published; Fits in one request

Gemma 4 26B A4B has no comparable published API token rate. Qwen3.6 Plus has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Gemma 4 26B A4B: Self-hosted; infrastructure cost varies; Fits in one request
Qwen3.6 Plus: API rate not published; Fits in one request

Gemma 4 26B A4B has no comparable published API token rate. Qwen3.6 Plus has no comparable published API token rate.

Cache-heavy agent loop

200K cached + 20K fresh input + 10K output tokens

Gemma 4 26B A4B: Self-hosted; infrastructure cost varies; Fits in one request; Cached-input rate unavailable
Qwen3.6 Plus: API rate not published; Fits in one request; Cached-input rate unavailable

Gemma 4 26B A4B has no comparable published API token rate. Qwen3.6 Plus 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 26B A4BQwen3.6 Plus

Context window

Maximum documented context; output-token limits may be lower.

Gemma 4 26B A4B

256K

Google Gemma 4 model documentation

Qwen3.6 Plus

API model ID

Gemma 4 26B A4B

gemma-4-26b-a4b-it

Google Gemma Gemini API guide

Qwen3.6 Plus

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 26B A4B

No comparable hosted API rate

Qwen3.6 Plus

No comparable hosted API rate

Documented inputs

Gemma 4 26B A4B

text, image

Google Gemma 4 model documentation

Qwen3.6 Plus

text, image, video

Qwen Cloud model page for qwen3.6-plus

Documented outputs

Gemma 4 26B A4B

text

Google Gemma 4 model documentation

Qwen3.6 Plus

text

Qwen Cloud model page for qwen3.6-plus

Provider availability

Gemma 4 26B A4B

Generally Available · Gemini API, Google AI Studio, open weights

Google Gemma Gemini API guide

Qwen3.6 Plus

Not sourced

Reasoning profile

Gemma 4 26B A4B

Reasoning

Qwen3.6 Plus

Reasoning

Weight access

Gemma 4 26B A4B

Open Weight

Qwen3.6 Plus

Proprietary

License

Gemma 4 26B A4B

Open Weight

Qwen3.6 Plus

Proprietary

Release date

Gemma 4 26B A4B

2026-04-02

Qwen3.6 Plus

2026-04-02

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: Qwen3.6 Plus has the higher public point estimate, 53.41 versus 41.23. 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: Qwen3.6 Plus has the larger documented window (1M).

Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, Gemma 4 26B A4B or Qwen3.6 Plus?

Qwen3.6 Plus has the higher public point estimate, 53.41 versus 41.23. 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 26B A4B or Qwen3.6 Plus?

Qwen3.6 Plus has the higher public coding point estimate, 39.8 to 30.6, with Supported evidence for both models. The conditional ranges do not establish rank confidence.

Which is better for agentic tasks, Gemma 4 26B A4B or Qwen3.6 Plus?

Gemma 4 26B A4B is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, Gemma 4 26B A4B or Qwen3.6 Plus?

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 26B A4B or Qwen3.6 Plus?

Qwen3.6 Plus has the larger documented context window: 1M, compared with 256K.

Self-host vs API cost

Estimates at 50,000 req/day · 1000 tokens/req average.

Gemma 4 26B A4B

API / mo (Novita AI)$398

Self-host / mo$298

Break-even37M/day

Qwen3.6 Plus

API / moNo API price on file

Self-host / moNot listed

Break-even—

Proprietary model — self-hosting not applicable.

Model the full break-even

Benchmark evidence

The full public result ledger is available for audit without forcing a wide desktop table onto a phone.

Browse raw public benchmark evidence54 rows

Agentic

Terminal-Bench 2.0
Gemma 4 26B A4B—
Qwen3.6 Plus61.6%
Source
Not directly comparable
Claw-Eval
Gemma 4 26B A4B—
Qwen3.6 Plus58.8%
Source
Not directly comparable
QwenClawBench
Gemma 4 26B A4B—
Qwen3.6 Plus57.2%
Source
Not directly comparable
τ³-bench results
Gemma 4 26B A4B—
Qwen3.6 Plus70.7%
Source
Not directly comparable
VITA-Bench
Gemma 4 26B A4B—
Qwen3.6 Plus44.3%
Source
Not directly comparable
DeepPlanning
Gemma 4 26B A4B—
Qwen3.6 Plus41.5%
Source
Not directly comparable
Toolathlon
Gemma 4 26B A4B—
Qwen3.6 Plus39.8%
Source
Not directly comparable
MCP Atlas
Gemma 4 26B A4B—
Qwen3.6 Plus48.2%
Source
Not directly comparable
MCP-Tasks
Gemma 4 26B A4B—
Qwen3.6 Plus74.1%
Source
Not directly comparable
WideResearch
Gemma 4 26B A4B—
Qwen3.6 Plus74.3%
Source
Not directly comparable
Gert Labs
Gemma 4 26B A4B—
Qwen3.6 Plus50.60%
Source
Not directly comparable
ResearchClawBench
Gemma 4 26B A4B—
Qwen3.6 Plus18.0%
Source
Not directly comparable
Terminal-Bench 2.1 (Vals)
Gemma 4 26B A4B—
Qwen3.6 Plus53.2%
Source
Not directly comparable

Coding

ScarfBench
Gemma 4 26B A4B24.4%
Source
Qwen3.6 Plus—
Not directly comparable
LiveCodeBench v6
Gemma 4 26B A4B77.1%
Source
Qwen3.6 Plus87.1%
Source
Qwen3.6 Plus leads this result
SWE-bench Verified
Gemma 4 26B A4B—
Qwen3.6 Plus78.8%
Source
Not directly comparable
SWE-bench Pro
Gemma 4 26B A4B—
Qwen3.6 Plus56.6%
Source
Not directly comparable
SWE Multilingual
Gemma 4 26B A4B—
Qwen3.6 Plus73.8%
Source
Not directly comparable
NL2Repo
Gemma 4 26B A4B—
Qwen3.6 Plus37.9%
Source
Not directly comparable
Terminal-Bench 2.0
Gemma 4 26B A4B—
Qwen3.6 Plus61.6%
Source
Not directly comparable
Vibe Code Bench
Gemma 4 26B A4B—
Qwen3.6 Plus25.56%
Source
Not directly comparable
LiveCodeBench (Vals)
Gemma 4 26B A4B—
Qwen3.6 Plus86.0%
Source
Not directly comparable
SWE-bench (Vals)
Gemma 4 26B A4B—
Qwen3.6 Plus73.4%
Source
Not directly comparable

Reasoning

MRCRv2
Gemma 4 26B A4B44.1%
Source
Qwen3.6 Plus—
Not directly comparable
AI-Needle
Gemma 4 26B A4B—
Qwen3.6 Plus68.3%
Source
Not directly comparable
LongBench v2
Gemma 4 26B A4B—
Qwen3.6 Plus62%
Source
Not directly comparable

Multimodal

ExtractBench (LlamaIndex)
Gemma 4 26B A4B70.27%
Source
Qwen3.6 Plus—
Not directly comparable
ParseBench-1
Gemma 4 26B A4B58.50%
Source
Qwen3.6 Plus—
Not directly comparable
MMMU-Pro
Gemma 4 26B A4B73.8%
Source
Qwen3.6 Plus78.8%
Source
Qwen3.6 Plus leads this result
MMMU
Gemma 4 26B A4B—
Qwen3.6 Plus86.0%
Source
Not directly comparable
MathVision
Gemma 4 26B A4B—
Qwen3.6 Plus88.0%
Source
Not directly comparable
VideoMMMU
Gemma 4 26B A4B—
Qwen3.6 Plus84.0%
Source
Not directly comparable
ScreenSpot Pro
Gemma 4 26B A4B—
Qwen3.6 Plus68.2%
Source
Not directly comparable
CharXiv
Gemma 4 26B A4B—
Qwen3.6 Plus81.5%
Source
Not directly comparable
V*
Gemma 4 26B A4B—
Qwen3.6 Plus96.9%
Source
Not directly comparable

Knowledge

GPQA
Gemma 4 26B A4B82.3%
Source
Qwen3.6 Plus90.4%
Source
Qwen3.6 Plus leads this result
MMLU-Pro
Gemma 4 26B A4B82.6%
Source
Qwen3.6 Plus88.5%
Source
Qwen3.6 Plus leads this result
HLE
Gemma 4 26B A4B17.2%
Source
Qwen3.6 Plus28.8%
Source
Qwen3.6 Plus leads this result
HLE w/o tools
Gemma 4 26B A4B8.7%
Source
Qwen3.6 Plus—
Not directly comparable
SuperGPQA
Gemma 4 26B A4B—
Qwen3.6 Plus71.6%
Source
Not directly comparable
MMLU-Redux
Gemma 4 26B A4B—
Qwen3.6 Plus94.5%
Source
Not directly comparable
C-Eval
Gemma 4 26B A4B—
Qwen3.6 Plus93.3%
Source
Not directly comparable
GPQA Diamond (Vals)
Gemma 4 26B A4B—
Qwen3.6 Plus87.4%
Source
Not directly comparable
MMLU-Pro (Vals)
Gemma 4 26B A4B—
Qwen3.6 Plus87.7%
Source
Not directly comparable

Multilingual

MMLU-ProX
Gemma 4 26B A4B—
Qwen3.6 Plus84.7%
Source
Not directly comparable
NOVA-63
Gemma 4 26B A4B—
Qwen3.6 Plus57.9%
Source
Not directly comparable

Instruction following

IFEval
Gemma 4 26B A4B—
Qwen3.6 Plus94.3%
Source
Not directly comparable
IFBench
Gemma 4 26B A4B—
Qwen3.6 Plus75.8%
Source
Not directly comparable

Math

AIME26
Gemma 4 26B A4B—
Qwen3.6 Plus95.3%
Source
Not directly comparable
HMMT Feb 2025
Gemma 4 26B A4B—
Qwen3.6 Plus96.7%
Source
Not directly comparable
HMMT Nov 2025
Gemma 4 26B A4B—
Qwen3.6 Plus94.6%
Source
Not directly comparable
HMMT Feb 2026
Gemma 4 26B A4B—
Qwen3.6 Plus87.8%
Source
Not directly comparable
MMAnswerBench
Gemma 4 26B A4B—
Qwen3.6 Plus83.8%
Source
Not directly comparable
FrontierMath v2 (Tiers 1-3)
Gemma 4 26B A4B—
Qwen3.6 Plus38.246%
Source
Not directly comparable

54 public results · 5 shared

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Last updated November 13, 2026

Gemma 4 26B A4B vs Qwen3.6 Plus

Decision reading

Which one for your work

Coding work

Long documents

Agentic work

Chat turn cost

Cache-heavy agent loop cost

Repository review cost

Which one for a specific job

Or describe your own job

What is actually comparable

Shape of the matched evidence

Category results, on a stated basis

Coding

Reasoning

Multimodal

Knowledge

Instruction following

Agentic

Multilingual

Math

What each workload costs

Chat turn

Repository review

Cache-heavy agent loop

Specification differences

Context window

API model ID

Cached-input rate

Documented inputs

Documented outputs

Provider availability

Reasoning profile

Weight access

License

Release date

If you already use one of these models

Questions

Which is better, Gemma 4 26B A4B or Qwen3.6 Plus?

Which is better for coding, Gemma 4 26B A4B or Qwen3.6 Plus?

Which is better for agentic tasks, Gemma 4 26B A4B or Qwen3.6 Plus?

Which costs less, Gemma 4 26B A4B or Qwen3.6 Plus?

Which has the larger context window, Gemma 4 26B A4B or Qwen3.6 Plus?

Self-host vs API cost

Benchmark evidence

Agentic

Coding

Reasoning

Multimodal

Knowledge

Multilingual

Instruction following

Math

Related comparisons

Watch Gemma 4 26B A4B vs Qwen3.6 Plus