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Model comparison

Gemini 3.6 Flash vs Grok 4.6

Updated August 12, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Gemini 3.6 Flash

Google

75.5/100

Supported · Public rank #10

90% interval 71.6–79.4

Grok 4.6

xAI

Evidence status unavailable

90% interval unavailable

At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.

2 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

    Gemini 3.6 Flash

    Gemini 3.6 Flash has the larger documented context window.

    Confidence: documented

  • Chat turn cost

    1K fresh input + 500 output tokens

    Grok 4.6

    Grok 4.6 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.

    Confidence: listed-rates

  • Cache-heavy agent loop cost

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

    Gemini 3.6 Flash

    Gemini 3.6 Flash has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.

    Confidence: listed-rates

Show secondary and unsupported calls
  • Repository review cost

    50K fresh input + 3K output tokens

    Gemini 3.6 Flash

    Gemini 3.6 Flash has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.

    Confidence: listed-rates

  • 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

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
2
Gemini 3.6 Flash only
1
Grok 4.6 only
3
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
Gemini 3.6 Flash
83.0
Grok 4.6
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Coding

Not comparable
Gemini 3.6 Flash
Not measured
Grok 4.6
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Reasoning

Not comparable
Gemini 3.6 Flash
Not measured
Grok 4.6
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Knowledge

Not comparable
Gemini 3.6 Flash
Not measured
Grok 4.6
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Math

Not comparable
Gemini 3.6 Flash
Not measured
Grok 4.6
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
Gemini 3.6 Flash
Not measured
Grok 4.6
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
Gemini 3.6 Flash
Not measured
Grok 4.6
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
Gemini 3.6 Flash
Not measured
Grok 4.6
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

Gemini 3.6 Flash
$0.00525
Fits in one request
Grok 4.6
$0.005
Fits in one request

Grok 4.6 has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

Gemini 3.6 Flash
$0.0975
Fits in one request
Grok 4.6
$0.118
Fits in one request

Gemini 3.6 Flash has the lower modeled cost

Costs use the listed standard API rates.

Cache-heavy agent loop

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

Gemini 3.6 Flash
$0.135
Fits in one request
Grok 4.6
$0.2
Fits in one request

Gemini 3.6 Flash has the lower modeled cost

Costs use the listed standard API rates.

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.

Gemini 3.6 Flash

$0.15 per 1M cached input tokens

Google Gemini API pricing

Grok 4.6

$0.5 per 1M cached input tokens

xAI Grok 4.6 release notes

Reasoning profile

Gemini 3.6 Flash

Reasoning

Grok 4.6

Reasoning

Weight access

Gemini 3.6 Flash

Proprietary

Grok 4.6

Proprietary

License

Gemini 3.6 Flash

Proprietary

Grok 4.6

Proprietary

Release date

Gemini 3.6 Flash

2026-07-21

Grok 4.6

2026-08-12

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
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.
Workload cost
Repository review: $0.0975 vs $0.118. Cache-heavy agent loop: $0.135 vs $0.2.
Context tradeoff
Gemini 3.6 Flash 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

  • OSWorld-Verified

    Gemini 3.6 Flash83%
    Source
    Grok 4.6

    Not directly comparable

  • Terminal-Bench 3.0

    Gemini 3.6 Flash
    Grok 4.626.0%
    Source

    Not directly comparable

  • APEX-Agents

    Gemini 3.6 Flash
    Grok 4.657.5%
    Source

    Not directly comparable

Coding

  • deepSwe

    Gemini 3.6 Flash49%
    Source
    Grok 4.665.9%
    Source

    Grok 4.6 leads this result

  • cursorBench32

    Gemini 3.6 Flash53.5%
    Source
    Grok 4.669.9%
    Source

    Grok 4.6 leads this result

  • FrontierCode 1.1 Extended

    Gemini 3.6 Flash
    Grok 4.661.3%
    Source

    Not directly comparable

Frequently asked questions

Which is better, Gemini 3.6 Flash or Grok 4.6?

At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, Gemini 3.6 Flash or Grok 4.6?

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, Gemini 3.6 Flash or Grok 4.6?

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, Gemini 3.6 Flash or Grok 4.6?

For the stated presets, chat costs $0.00525 on Gemini 3.6 Flash and $0.005 on Grok 4.6; repository review costs $0.0975 and $0.118; the cache-heavy agent loop costs $0.135 and $0.2. Costs use the listed standard API rates.

Which has the larger context window, Gemini 3.6 Flash or Grok 4.6?

Gemini 3.6 Flash has the larger documented context window: 1M, compared with 500K.

Related comparisons

Last updated August 12, 2026

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