Skip to main content
BenchLM
Advertise

Model comparison

Gemma 4 26B A4B vs Grok 4.3

Updated July 30, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Gemma 4 26B A4B

Google

57.1/100

Supported · Public rank #74

90% interval 40.0–74.2

Grok 4.3

xAI

64.2/100

Supported · Public rank #36

90% interval 54.5–74.0

The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. Use the documented cost, context, and runtime rows instead.

0 results are shared. Category rows based on different benchmark sets are marked directional and do not name a winner.

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.

  • Long documents

    Prompts that approach the documented context limit

    Grok 4.3

    Grok 4.3 has the larger documented context window.

    Confidence: documented

Show secondary and unsupported calls

  • Coding work

    Code generation, repair, and software-engineering tasks

    Not enough matched evidence

    No shared weighted benchmark basis supports a winner.

    Confidence: limited

  • Agentic work

    Tool use, computer use, and multi-step task completion

    Not enough matched evidence

    No shared weighted benchmark basis supports a winner.

    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

What is actually comparable

Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.

Shared results
0
Gemma 4 26B A4B only
4
Grok 4.3 only
2
Like-for-like categories
0 / 8

Category results, on a stated basis

Each row states whether both averages use the same weighted benchmark set. Directional and not-comparable rows remain visible, but they never receive a winner in this template.

Agentic

Not comparable
Gemma 4 26B A4B
Not measured
Grok 4.3
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Coding

Not comparable
Gemma 4 26B A4B
Not measured
Grok 4.3
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Reasoning

Not comparable
Gemma 4 26B A4B
Not measured
Grok 4.3
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Knowledge

Not comparable
Gemma 4 26B A4B
43.4
Grok 4.3
Not measured
Weighted basis
2 vs 0 rows
Reading
Not comparable

Math

Not comparable
Gemma 4 26B A4B
Not measured
Grok 4.3
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
Gemma 4 26B A4B
Not measured
Grok 4.3
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
Gemma 4 26B A4B
73.8
Grok 4.3
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
Gemma 4 26B A4B
Not measured
Grok 4.3
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

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.

A shared-evidence shape is not available.

BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.

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
Grok 4.3
$0.0025
Fits in one request

Gemma 4 26B A4B 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
Grok 4.3
$0.07
Fits in one request

Gemma 4 26B A4B 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
Grok 4.3
$0.09
Fits in one request

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

Specification differences

Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.

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

Grok 4.3

$0.2 per 1M cached input tokens

Provider availability

Gemma 4 26B A4B

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

Google Gemma Gemini API guide

Grok 4.3

Not sourced

Reasoning profile

Gemma 4 26B A4B

Reasoning

Grok 4.3

Reasoning

Weight access

Gemma 4 26B A4B

Open Weight

Grok 4.3

Proprietary

License

Gemma 4 26B A4B

Open Weight

Grok 4.3

Proprietary

Release date

Gemma 4 26B A4B

2026-04-02

Grok 4.3

2026-04-30

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
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict.
Workload cost
A complete comparable API-rate estimate is not available for both models.
Context tradeoff
Grok 4.3 has the larger documented window (1M).

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

Benchmark evidence

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

Browse raw public benchmark evidence6 rows

Agentic

  • Gert Labs

    Gemma 4 26B A4B—
    Grok 4.343.86%
    Source

    Not directly comparable

  • ResearchClawBench

    Gemma 4 26B A4B—
    Grok 4.312.4%
    Source

    Not directly comparable

Knowledge

  • MMLU-Pro

    Gemma 4 26B A4B82.6%
    Source
    Grok 4.3—

    Not directly comparable

  • HLE

    Gemma 4 26B A4B17.2%
    Source
    Grok 4.3—

    Not directly comparable

  • HLE w/o tools

    Gemma 4 26B A4B8.7%
    Source
    Grok 4.3—

    Not directly comparable

Multimodal

  • MMMU-Pro

    Gemma 4 26B A4B73.8%
    Source
    Grok 4.3—

    Not directly comparable

Frequently asked questions

Which is better, Gemma 4 26B A4B or Grok 4.3?

The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, Gemma 4 26B A4B or Grok 4.3?

The published evidence does not provide a shared weighted coding basis for both models, so BenchLM does not name a coding winner.

Which is better for agentic tasks, Gemma 4 26B A4B or Grok 4.3?

The published evidence does not provide a shared weighted agentic tasks basis for both models, so BenchLM does not name a agentic tasks winner.

Which costs less, Gemma 4 26B A4B or Grok 4.3?

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 Grok 4.3?

Grok 4.3 has the larger documented context window: 1M, compared with 256K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated July 30, 2026

Watch Gemma 4 26B A4B vs Grok 4.3

One weekly email when material rank, price, or benchmark evidence changes make this matchup worth revisiting.

Read a sample issue

Join 2,000+ readers.