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Gemini 4 Carbon vs Ripple-65B Preview

Updated November 4, 2026. We do not rank this pair: at least one has no public score. Public scores include evidence status and uncertainty.

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Decision reading

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

88.38/100

Estimated · Public rank #1

Conditional range 78.7–98.1

Model B

Ricursive Intelligence

—

Evidence status unavailable

90% interval unavailable

Shared results
1
Gemini 4 Carbon only
8
Ripple-65B Preview only
16
Like-for-like categories
0 / 8
Estimated: Gemini 4 Carbon. Conditional ranges do not establish rank confidence.How 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.

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

    Ripple-65B Preview is not ranked on the public lane for coding, so no winner is named for coding.

    Confidence: limited
  • Agentic work

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

    Not enough matched evidence

    Gemini 4 Carbon and Ripple-65B Preview are not ranked on the public lane for agentic, so no winner is named for agentic.

    Confidence: limited
  • Long documents

    Prompts that approach the documented context limit

    Not enough matched evidence

    A complete context comparison is not sourced.

    Confidence: limited
  • 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

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.

81.1Gemini 4 Carbon—Ripple-65B Preview

Not comparable · BenchAlign v5.8

The coding row is not comparable on the public lane: at least one model is not measured or not ranked there.

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.

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.

Bars run 0–100 on each benchmark’s normalized display scale

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign v5.8 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.

Agentic

Not comparable
Gemini 4 Carbon
Not ranked
Ripple-65B Preview
Not ranked
Basis
BenchAlign v5.8 lane · 4 vs 4 public rows
Reading
Not comparable

Coding

Not comparable
Gemini 4 Carbon
81.1
Supported · #3/156
Ripple-65B Preview
Not ranked
Basis
BenchAlign v5.8 lane · 1 vs 5 public rows
Reading
Not comparable

Reasoning

Not comparable
Gemini 4 Carbon
79.5
Unranked · 2 rankable rows
Ripple-65B Preview
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
Gemini 4 Carbon
Not ranked
Ripple-65B Preview
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Knowledge

Not comparable
Gemini 4 Carbon
88.2
Supported · #2/179
Ripple-65B Preview
Not ranked
Basis
BenchAlign v5.8 lane · 0 vs 3 public rows
Reading
Not comparable

Multilingual

Not comparable
Gemini 4 Carbon
Not ranked
Ripple-65B Preview
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
Gemini 4 Carbon
Not ranked
Ripple-65B Preview
Not ranked
Basis
Provisional lane · 0 vs 1 weighted rows
Reading
Not comparable

Math

Not comparable
Gemini 4 Carbon
Not ranked
Ripple-65B Preview
Not ranked
Basis
Provisional lane · 0 vs 2 weighted rows
Reading
Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.8) 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 4 Carbon
$0.007
Fit state unavailable
Ripple-65B Preview
API rate not published
Fits in one request

Ripple-65B Preview has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Gemini 4 Carbon
$0.13
Fit state unavailable
Ripple-65B Preview
API rate not published
Fits in one request

Ripple-65B Preview has no comparable published API token rate.

Cache-heavy agent loop

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

Gemini 4 Carbon
$0.16
Fit state unavailable
Ripple-65B Preview
API rate not published
Fits in one request
Cached-input rate unavailable

Ripple-65B Preview has no comparable published API token 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.

Reasoning profile

Gemini 4 Carbon

Reasoning

Ripple-65B Preview

Reasoning

Weight access

Gemini 4 Carbon

Proprietary

Ripple-65B Preview

Open Weight

License

Gemini 4 Carbon

Proprietary

Ripple-65B Preview

Open Weight

Release date

Gemini 4 Carbon

2026-10-28

Ripple-65B Preview

2026-11-04

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
A complete comparable API-rate estimate is not available for both models.
Context tradeoff
A complete documented context comparison is not available.
Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, Gemini 4 Carbon or Ripple-65B Preview?

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 4 Carbon or Ripple-65B Preview?

Ripple-65B Preview is not ranked on the public lane for coding, so no winner is named for coding.

Which is better for agentic tasks, Gemini 4 Carbon or Ripple-65B Preview?

Gemini 4 Carbon and Ripple-65B Preview are not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, Gemini 4 Carbon or Ripple-65B Preview?

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 4 Carbon or Ripple-65B Preview?

A complete documented context-window comparison is not available.

Self-host vs API cost

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

Gemini 4 Carbon
API / mo$9,000
Self-host / moNot listed
Break-even—
Proprietary model — self-hosting not applicable.
Ripple-65B Preview
API / mo$0
Self-host / mo$1,752
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 evidence25 rows

Agentic

  • AutomationBench

    Gemini 4 Carbon53.9%
    Source
    Ripple-65B Preview36.4%
    Source

    Gemini 4 Carbon leads this result

  • OSWorld 2.1 Offline

    Gemini 4 Carbon81.5%
    Source
    Ripple-65B Preview—

    Not directly comparable

  • CWE-bench v1

    Gemini 4 Carbon71.0%
    Source
    Ripple-65B Preview—

    Not directly comparable

  • ExploitGym

    Gemini 4 Carbon53.5%
    Source
    Ripple-65B Preview—

    Not directly comparable

  • Terminal-Bench 2.1

    Gemini 4 Carbon—
    Ripple-65B Preview80.5%
    Source

    Not directly comparable

  • τ³-bench results

    Gemini 4 Carbon—
    Ripple-65B Preview31.3%
    Source

    Not directly comparable

  • MCP Atlas

    Gemini 4 Carbon—
    Ripple-65B Preview71.7%
    Source

    Not directly comparable

Coding

  • FrontierSWE v2

    Gemini 4 Carbon61.2%
    Source
    Ripple-65B Preview—

    Not directly comparable

  • Terminal-Bench 2.1

    Gemini 4 Carbon—
    Ripple-65B Preview80.5%
    Source

    Not directly comparable

  • SWE-bench Verified

    Gemini 4 Carbon—
    Ripple-65B Preview76.5%
    Source

    Not directly comparable

  • SWE-bench Pro

    Gemini 4 Carbon—
    Ripple-65B Preview61.4%
    Source

    Not directly comparable

  • DeepSWE

    Gemini 4 Carbon—
    Ripple-65B Preview44.3%
    Source

    Not directly comparable

  • SciCode

    Gemini 4 Carbon—
    Ripple-65B Preview39.4%
    Source

    Not directly comparable

Reasoning

  • GraphWalks BFS 256K–1M

    Gemini 4 Carbon96.2%
    Source
    Ripple-65B Preview—

    Not directly comparable

Multimodal

  • Chartography (no tools)

    Gemini 4 Carbon72.5%
    Source
    Ripple-65B Preview—

    Not directly comparable

  • Blueprint-Bench 2

    Gemini 4 Carbon58.6%
    Source
    Ripple-65B Preview—

    Not directly comparable

Knowledge

  • MMLU

    Gemini 4 Carbon—
    Ripple-65B Preview89.6%
    Source

    Not directly comparable

  • MMLU-Pro

    Gemini 4 Carbon—
    Ripple-65B Preview84.2%
    Source

    Not directly comparable

  • GPQA-D

    Gemini 4 Carbon—
    Ripple-65B Preview85.2%
    Source

    Not directly comparable

Instruction following

  • Gray Swan IPI (15 attempts)

    Gemini 4 Carbon0.0%
    Source
    Ripple-65B Preview—

    Not directly comparable

  • IFBench

    Gemini 4 Carbon—
    Ripple-65B Preview79.2%
    Source

    Not directly comparable

Math

  • AIME 2024

    Gemini 4 Carbon—
    Ripple-65B Preview97.1%
    Source

    Not directly comparable

  • AIME 2025

    Gemini 4 Carbon—
    Ripple-65B Preview98.3%
    Source

    Not directly comparable

  • AIME26

    Gemini 4 Carbon—
    Ripple-65B Preview95.0%
    Source

    Not directly comparable

  • HMMT Feb 2026

    Gemini 4 Carbon—
    Ripple-65B Preview90.5%
    Source

    Not directly comparable

25 public results · 1 shared

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Last updated November 4, 2026