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Model A
Gemini 2.5 Flash

Google

47.8/100

Supported · Public rank #158

90% interval 24.471.2

Gemini 2.5 Flash vs Llama 4 Maverick

Updated September 3, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Meta logo
Model B
Llama 4 Maverick

Meta

22.85/100

Supported · Public rank #226

90% interval 15.430.3

Decision reading

Gemini 2.5 Flash has the higher public score estimate, 47.8 versus 22.85, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.

1 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.

No workload recommendation clears the current evidence threshold.

Use the matched evidence, workload costs, and sourced specifications below instead of treating a point score as a universal answer.

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

  • Long documents

    Prompts that approach the documented context limit

    No clear pick

    The documented context windows are equal.

    Confidence: documented

  • 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
1
Gemini 2.5 Flash only
1
Llama 4 Maverick only
0
Like-for-like categories
0 / 8

1 category uses different evidence sets. Those rows remain visible for coverage context but do not name a winner.

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.

Math

Directional only
Gemini 2.5 Flash
4.7
Llama 4 Maverick
0.7
Weighted basis
2 vs 1 rows
Reading
Directional only

Agentic

Not comparable
Gemini 2.5 Flash
Not measured
Llama 4 Maverick
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Coding

Not comparable
Gemini 2.5 Flash
Not measured
Llama 4 Maverick
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Reasoning

Not comparable
Gemini 2.5 Flash
Not measured
Llama 4 Maverick
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Knowledge

Not comparable
Gemini 2.5 Flash
Not measured
Llama 4 Maverick
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
Gemini 2.5 Flash
Not measured
Llama 4 Maverick
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
Gemini 2.5 Flash
Not measured
Llama 4 Maverick
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
Gemini 2.5 Flash
Not measured
Llama 4 Maverick
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.

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.

  • FrontierMath v2 (Tiers 1-3)

    Math

    Gemini 2.5 Flash: 4.844%Llama 4 Maverick: 0.690%Normalized gap 4.2Shared source

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 2.5 Flash
$0.00155
Fits in one request
Llama 4 Maverick
Self-hosted; infrastructure cost varies
Fits in one request

Llama 4 Maverick has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Gemini 2.5 Flash
$0.0225
Fits in one request
Llama 4 Maverick
Self-hosted; infrastructure cost varies
Fits in one request

Llama 4 Maverick has no comparable published API token rate.

Cache-heavy agent loop

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

Gemini 2.5 Flash
$0.037
Fits in one request
Llama 4 Maverick
Self-hosted; infrastructure cost varies
Fits in one request
Cached-input rate unavailable

Llama 4 Maverick 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.

Context window

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

Gemini 2.5 Flash

Llama 4 Maverick

1M

Cached-input rate

A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.

Gemini 2.5 Flash

$0.03 per 1M cached input tokens

Google Gemini API pricing

Llama 4 Maverick

No comparable hosted API rate

Documented inputs

Gemini 2.5 Flash

Not sourced

Llama 4 Maverick

Not sourced

Documented outputs

Gemini 2.5 Flash

Not sourced

Llama 4 Maverick

Not sourced

Provider availability

Gemini 2.5 Flash

Not sourced

Llama 4 Maverick

Not sourced

Reasoning profile

Gemini 2.5 Flash

Non-Reasoning

Llama 4 Maverick

Non-Reasoning

Weight access

Gemini 2.5 Flash

Proprietary

Llama 4 Maverick

Open Weight

License

Gemini 2.5 Flash

Proprietary

Llama 4 Maverick

Open Weight

Release date

Gemini 2.5 Flash

2025-06-17

Llama 4 Maverick

2026-02-28

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
Gemini 2.5 Flash has the higher public score estimate, 47.8 versus 22.85, but the 90% score intervals overlap.
Workload cost
A complete comparable API-rate estimate is not available for both models.
Context tradeoff
Both models list 1M.

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

Self-host vs API cost

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

Gemini 2.5 Flash
API / mo$2,100
Self-host / moNot listed
Break-even
Proprietary model — self-hosting not applicable.
Llama 4 Maverick
API / mo$0
Self-host / mo$2,610
Break-even
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 evidence2 rows

Math

  • FrontierMath v2 (Tiers 1-3)

    Shared source
    Gemini 2.5 Flash4.844%
    Llama 4 Maverick0.690%

    Gemini 2.5 Flash leads this result

  • FrontierMath v2 (Tier 4)

    Gemini 2.5 Flash4.167%
    Source
    Llama 4 Maverick

    Not directly comparable

Frequently asked questions

Which is better, Gemini 2.5 Flash or Llama 4 Maverick?

Gemini 2.5 Flash has the higher public score estimate, 47.8 versus 22.85, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.

Which is better for coding, Gemini 2.5 Flash or Llama 4 Maverick?

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 2.5 Flash or Llama 4 Maverick?

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 2.5 Flash or Llama 4 Maverick?

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, Gemini 2.5 Flash or Llama 4 Maverick?

Both models list the same context window, 1M.

Related comparisons

Last updated September 3, 2026

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