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Model comparison

Gemini 3.6 Flash vs Inkling-Small

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

Gemini 3.6 Flash

Google

75.3/100

Supported · Public rank #9

90% interval 71.5–79.1

Inkling-Small

Thinking Machines Lab

Evidence status unavailable

90% interval unavailable

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.

  • Chat turn cost

    1K fresh input + 500 output tokens

    Inkling-Small

    Inkling-Small 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

    Inkling-Small

    Inkling-Small 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

    Inkling-Small

    Inkling-Small 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

  • Long documents

    Prompts that approach the documented context limit

    No clear pick

    The documented context windows are equal.

    Confidence: documented

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
Gemini 3.6 Flash only
3
Inkling-Small only
20
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
Inkling-Small
70.1
Weighted basis
1 vs 2 rows
Reading
Not comparable

Coding

Not comparable
Gemini 3.6 Flash
Not measured
Inkling-Small
62.4
Weighted basis
0 vs 3 rows
Reading
Not comparable

Reasoning

Not comparable
Gemini 3.6 Flash
Not measured
Inkling-Small
40.1
Weighted basis
0 vs 1 rows
Reading
Not comparable

Knowledge

Not comparable
Gemini 3.6 Flash
Not measured
Inkling-Small
53.4
Weighted basis
0 vs 2 rows
Reading
Not comparable

Math

Not comparable
Gemini 3.6 Flash
Not measured
Inkling-Small
92.9
Weighted basis
0 vs 2 rows
Reading
Not comparable

Multilingual

Not comparable
Gemini 3.6 Flash
Not measured
Inkling-Small
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
Gemini 3.6 Flash
Not measured
Inkling-Small
76.6
Weighted basis
0 vs 2 rows
Reading
Not comparable

Instruction following

Not comparable
Gemini 3.6 Flash
Not measured
Inkling-Small
82.2
Weighted basis
0 vs 1 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
Inkling-Small
$0.0013
Fits in one request

Inkling-Small 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
Inkling-Small
$0.03332
Fits in one request

Inkling-Small 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
Inkling-Small
$0.0492
Fits in one request

Inkling-Small 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

Inkling-Small

$0.116 per 1M cached input tokens

Reasoning profile

Gemini 3.6 Flash

Reasoning

Inkling-Small

Hybrid

Weight access

Gemini 3.6 Flash

Proprietary

Inkling-Small

Open Weight

License

Gemini 3.6 Flash

Proprietary

Inkling-Small

Open Weight

Release date

Gemini 3.6 Flash

2026-07-21

Inkling-Small

2026-07-30

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
Repository review: $0.0975 vs $0.03332. Cache-heavy agent loop: $0.135 vs $0.0492.
Context tradeoff
Both models list 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 evidence23 rows

Agentic

  • OSWorld-Verified

    Gemini 3.6 Flash83%
    Source
    Inkling-Small

    Not directly comparable

  • Terminal-Bench 2.0

    Gemini 3.6 Flash
    Inkling-Small64.7%
    Source

    Not directly comparable

  • BrowseComp

    Gemini 3.6 Flash
    Inkling-Small77.4%
    Source

    Not directly comparable

  • MCP Atlas

    Gemini 3.6 Flash
    Inkling-Small79.6%
    Source

    Not directly comparable

  • Toolathlon-Verified

    Gemini 3.6 Flash
    Inkling-Small54.4%
    Source

    Not directly comparable

Coding

  • deepSwe

    Gemini 3.6 Flash49%
    Source
    Inkling-Small

    Not directly comparable

  • cursorBench32

    Gemini 3.6 Flash53.5%
    Source
    Inkling-Small

    Not directly comparable

  • SWE-bench Verified

    Gemini 3.6 Flash
    Inkling-Small80.2%
    Source

    Not directly comparable

  • SWE-bench Pro

    Gemini 3.6 Flash
    Inkling-Small55.9%
    Source

    Not directly comparable

  • Terminal-Bench 2.0

    Gemini 3.6 Flash
    Inkling-Small64.7%
    Source

    Not directly comparable

  • SciCode

    Gemini 3.6 Flash
    Inkling-Small48.7%
    Source

    Not directly comparable

Reasoning

  • ARC-AGI-2

    Gemini 3.6 Flash
    Inkling-Small40.1%
    Source

    Not directly comparable

  • CritPt

    Gemini 3.6 Flash
    Inkling-Small8.3%
    Source

    Not directly comparable

Knowledge

  • GPQA

    Gemini 3.6 Flash
    Inkling-Small89.5%
    Source

    Not directly comparable

  • GPQA-D

    Gemini 3.6 Flash
    Inkling-Small89.5%
    Source

    Not directly comparable

  • HLE

    Gemini 3.6 Flash
    Inkling-Small47.8%
    Source

    Not directly comparable

  • HLE w/o tools

    Gemini 3.6 Flash
    Inkling-Small31.6%
    Source

    Not directly comparable

Math

  • AIME26

    Gemini 3.6 Flash
    Inkling-Small95.5%
    Source

    Not directly comparable

  • HMMT Feb 2026

    Gemini 3.6 Flash
    Inkling-Small90.2%
    Source

    Not directly comparable

Multimodal

  • MMMU-Pro

    Gemini 3.6 Flash
    Inkling-Small74%
    Source

    Not directly comparable

  • CharXiv

    Gemini 3.6 Flash
    Inkling-Small81.3%
    Source

    Not directly comparable

  • CharXiv w/o tools

    Gemini 3.6 Flash
    Inkling-Small77.4%
    Source

    Not directly comparable

Instruction following

  • IFBench

    Gemini 3.6 Flash
    Inkling-Small82.2%
    Source

    Not directly comparable

Frequently asked questions

Which is better, Gemini 3.6 Flash or Inkling-Small?

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, Gemini 3.6 Flash or Inkling-Small?

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 Inkling-Small?

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 Inkling-Small?

For the stated presets, chat costs $0.00525 on Gemini 3.6 Flash and $0.0013 on Inkling-Small; repository review costs $0.0975 and $0.03332; the cache-heavy agent loop costs $0.135 and $0.0492. Costs use the listed standard API rates.

Which has the larger context window, Gemini 3.6 Flash or Inkling-Small?

Both models list the same context window, 1M.

Related comparisons

Last updated July 30, 2026

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