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BenchLM

Gemini 3 Flash vs Trinity-Large-Thinking

Updated September 29, 2026. Rank says Gemini 3 Flash is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

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Decision reading

Gemini 3 Flash has the higher public score estimate, 55.58 versus 34.23, but the 90% score intervals overlap. Treat that as a lead, not a settled winner. 1 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A
Google logo

Google

55.58/100

Supported · Public rank #57

90% interval 40.4–70.7

Model B
Arcee AI logo

Arcee AI

34.23/100

Estimated · Public rank #144

90% interval 13.7–54.8

Shared results
1
Gemini 3 Flash only
10
Trinity-Large-Thinking only
4
Like-for-like categories
1 / 8
Supported: Gemini 3 Flash · Estimated: Trinity-Large-ThinkingHow 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

    Gemini 3 Flash

    Gemini 3 Flash leads on the public coding lane, 36.3 to 21, with Supported evidence for both models, although the 90% intervals overlap.

    Confidence: limited
  • 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
Show secondary and unsupported calls
  • 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
  • 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
  • Agentic work

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

    Not enough matched evidence

    Trinity-Large-Thinking is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.

    Confidence: limited

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.

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

36.3Gemini 3 Flash21.0Trinity-Large-Thinking

Like-for-like · BenchAlign v5.7

Gemini 3 Flash leads the like-for-like coding row, although the 90% intervals overlap.

Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.

Same basis rules as the category table below

What is actually comparable

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

3 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.

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.

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign v5.7 lane for agentic, coding, 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
Gemini 3 Flash
36.3
Supported · #73/142
Trinity-Large-Thinking
21.0
Supported · #119/142
Basis
BenchAlign v5.7 lane · 3 vs 1 public rows
Reading
Gemini 3 Flash leads · intervals overlap

Agentic

Directional only
Gemini 3 Flash
31.6
Supported · #76/117
Trinity-Large-Thinking
17.7
Estimated · #104/117
Basis
BenchAlign v5.7 lane · 4 vs 1 public rows
Reading
Directional only

Knowledge

Directional only
Gemini 3 Flash
54.6
Supported · #46/168
Trinity-Large-Thinking
35.9
Estimated · #114/168
Basis
BenchAlign v5.7 lane · 2 vs 2 public rows
Reading
Directional only

Instruction following

Directional only
Gemini 3 Flash
64.7
#70/124
Trinity-Large-Thinking
66.3
#68/124
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Directional only

Reasoning

Not comparable
Gemini 3 Flash
60.3
Unranked · 2 rankable rows
Trinity-Large-Thinking
48.3
Unranked · 2 rankable rows
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
Gemini 3 Flash
76.9
Unranked · 1 rankable row
Trinity-Large-Thinking
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multilingual

Not comparable
Gemini 3 Flash
Not ranked
Trinity-Large-Thinking
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
Gemini 3 Flash
50.0
Unranked · 2 rankable rows
Trinity-Large-Thinking
Not ranked
Basis
Provisional lane · 2 vs 0 weighted rows
Reading
Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.7) 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

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.

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.

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, 55.58 versus 34.23, 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.

Questions

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

Gemini 3 Flash has the higher public score estimate, 55.58 versus 34.23, 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?

Gemini 3 Flash leads the public coding lane, 36.3 to 21, with Supported evidence for both models, although the 90% intervals overlap.

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

Gemini 3 Flash scores higher for agentic tasks on the public lane, 31.6 to 17.7. Trinity-Large-Thinking is scored on Estimated evidence for agentic tasks, so the reading is directional rather than like-for-like. BenchLM does not name a winner for agentic tasks from a directional reading; read the shared benchmark rows directly and test the models on the same task set.

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.

Benchmark evidence

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

Browse raw public benchmark evidence15 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

  • Terminal-Bench 2.1 (Vals)

    Gemini 3 Flash53.9%
    Source
    Trinity-Large-Thinking—

    Not directly comparable

Coding

  • Vibe Code Bench

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

    Not directly comparable

  • LiveCodeBench (Vals)

    Gemini 3 Flash85.6%
    Source
    Trinity-Large-Thinking—

    Not directly comparable

  • SWE-bench (Vals)

    Gemini 3 Flash75.0%
    Source
    Trinity-Large-Thinking—

    Not directly comparable

  • SWE-bench Verified*

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

    Not directly comparable

Knowledge

  • GPQA Diamond (Vals)

    Gemini 3 Flash87.9%
    Source
    Trinity-Large-Thinking—

    Not directly comparable

  • MMLU-Pro (Vals)

    Gemini 3 Flash88.6%
    Source
    Trinity-Large-Thinking—

    Not directly comparable

  • 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

15 public results · 1 shared

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Last updated September 29, 2026