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Model A
Gemini 3 Flash

Google

59.7/100

Supported · Public rank #65

90% interval 40.6–78.8

Gemini 3 Flash vs Trinity-Large-Thinking

Updated August 14, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Model B
Trinity-Large-Thinking

Arcee AI

47.7/100

Supported · Public rank #143

90% interval 30.7–64.7

Decision reading

Gemini 3 Flash has the higher public score estimate, 59.74 versus 47.71, 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.

  • Long documents

    Prompts that approach the documented context limit

    Gemini 3 Flash

    Gemini 3 Flash has the larger documented context window.

    Confidence: documented

  • Chat turn cost

    1K fresh input + 500 output tokens

    Trinity-Large-Thinking

    Trinity-Large-Thinking 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 Flash

    Gemini 3 Flash has the lower estimated token cost for this stated workload. Trinity-Large-Thinking has no published cached-input rate, so cached tokens use its listed input rate.

    Confidence: rate-fallback

Show secondary and unsupported calls
  • Repository review cost

    50K fresh input + 3K output tokens

    Trinity-Large-Thinking

    Trinity-Large-Thinking 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
1
Gemini 3 Flash only
6
Trinity-Large-Thinking only
4
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 Flash
Not measured
Trinity-Large-Thinking
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Coding

Not comparable
Gemini 3 Flash
Not measured
Trinity-Large-Thinking
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Reasoning

Not comparable
Gemini 3 Flash
Not measured
Trinity-Large-Thinking
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Knowledge

Not comparable
Gemini 3 Flash
Not measured
Trinity-Large-Thinking
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Math

Not comparable
Gemini 3 Flash
27.8
Trinity-Large-Thinking
Not measured
Weighted basis
2 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
Gemini 3 Flash
Not measured
Trinity-Large-Thinking
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
Gemini 3 Flash
Not measured
Trinity-Large-Thinking
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
Gemini 3 Flash
Not measured
Trinity-Large-Thinking
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 Flash
$0.002
Fits in one request
Trinity-Large-Thinking
$0.0007
Fits in one request

Trinity-Large-Thinking has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

Gemini 3 Flash
$0.034
Fits in one request
Trinity-Large-Thinking
$0.0152
Fits in one request

Trinity-Large-Thinking 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 Flash
$0.05
Fits in one request
Trinity-Large-Thinking
$0.064
Fits in one request
Cached input priced at the published list-input rate

Gemini 3 Flash has the lower modeled cost

Trinity-Large-Thinking has no published cached-input rate, so cached tokens use its listed input 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.

Trinity-Large-Thinking

512K

Cached-input rate

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

Gemini 3 Flash

$0.05 per 1M cached input tokens

Google Gemini API pricing

Trinity-Large-Thinking

Not published

Documented inputs

Gemini 3 Flash

Not sourced

Trinity-Large-Thinking

Not sourced

Documented outputs

Gemini 3 Flash

Not sourced

Trinity-Large-Thinking

Not sourced

Provider availability

Gemini 3 Flash

Not sourced

Trinity-Large-Thinking

Not sourced

Reasoning profile

Gemini 3 Flash

Non-Reasoning

Trinity-Large-Thinking

Reasoning

Weight access

Gemini 3 Flash

Proprietary

Trinity-Large-Thinking

Open Weight

License

Gemini 3 Flash

Proprietary

Trinity-Large-Thinking

Open Weight

Release date

Gemini 3 Flash

2025-12-01

Trinity-Large-Thinking

2026-03-10

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 3 Flash has the higher public score estimate, 59.74 versus 47.71, but the 90% score intervals overlap.
Workload cost
Repository review: $0.034 vs $0.0152. Cache-heavy agent loop: $0.05 vs $0.064.
Context tradeoff
Gemini 3 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 evidence11 rows

Agentic

  • Claw-Eval

    Gemini 3 Flash49.2%
    Source
    Trinity-Large-Thinking

    Not directly comparable

  • Gemini 3 Flash56.63%
    Trinity-Large-Thinking32.55%

    Gemini 3 Flash leads this result

  • JobBench

    Gemini 3 Flash11.4%
    Source
    Trinity-Large-Thinking

    Not directly comparable

Coding

  • Vibe Code Bench

    Gemini 3 Flash20.20%
    Source
    Trinity-Large-Thinking

    Not directly comparable

  • EEBench

    Gemini 3 Flash19.5%
    Source
    Trinity-Large-Thinking

    Not directly comparable

  • SWE-bench Verified*

    Gemini 3 Flash
    Trinity-Large-Thinking63.2%
    Source

    Not directly comparable

Knowledge

  • GPQA-D

    Gemini 3 Flash
    Trinity-Large-Thinking76.3%
    Source

    Not directly comparable

  • MMLU-Pro (Arcee)

    Gemini 3 Flash
    Trinity-Large-Thinking83.4%
    Source

    Not directly comparable

Math

  • FrontierMath v2 (Tiers 1-3)

    Gemini 3 Flash35.640%
    Source
    Trinity-Large-Thinking

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    Gemini 3 Flash4.167%
    Source
    Trinity-Large-Thinking

    Not directly comparable

  • AIME25 (Arcee)

    Gemini 3 Flash
    Trinity-Large-Thinking96.3%
    Source

    Not directly comparable

Frequently asked questions

Which is better, Gemini 3 Flash or Trinity-Large-Thinking?

Gemini 3 Flash has the higher public score estimate, 59.74 versus 47.71, 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 3 Flash or Trinity-Large-Thinking?

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 Flash or Trinity-Large-Thinking?

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 Flash or Trinity-Large-Thinking?

For the stated presets, chat costs $0.002 on Gemini 3 Flash and $0.0007 on Trinity-Large-Thinking; repository review costs $0.034 and $0.0152; the cache-heavy agent loop costs $0.05 and $0.064. Trinity-Large-Thinking has no published cached-input rate, so cached tokens use its listed input rate.

Which has the larger context window, Gemini 3 Flash or Trinity-Large-Thinking?

Gemini 3 Flash has the larger documented context window: 1M, compared with 512K.

Related comparisons

Last updated August 14, 2026

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