Gemini 4 Carbon vs LFM2.5-24B-A2B

Updated October 28, 2026. Rank says Gemini 4 Carbon is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

Decision reading

Gemini 4 Carbon has the higher public point estimate, 88.42 versus 36.97. Their conditional score ranges do not overlap. These ranges do not establish rank confidence. 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

Gemini 4 Carbon

Google

88.42/100

Estimated · Public rank #1

Conditional range 74.1–100.0

Model B

LFM2.5-24B-A2B

LiquidAI

36.97/100

Estimated · Public rank #151

Conditional range 22.6–51.3

Shared results: 1
Gemini 4 Carbon only: 8
LFM2.5-24B-A2B only: 12
Like-for-like categories: 0 / 8

Estimated: Gemini 4 Carbon and LFM2.5-24B-A2B. 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.

Chat turn cost
1K fresh input + 500 output tokens
LFM2.5-24B-A2B
LFM2.5-24B-A2B 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
LFM2.5-24B-A2B
LFM2.5-24B-A2B 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
LFM2.5-24B-A2B 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 is 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
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Not enough matched evidence
The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. LFM2.5-24B-A2B does not fit this workload in one request.
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.

Pick the job you would use them for

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

82.2Gemini 4 Carbon—LFM2.5-24B-A2B

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.

Open the coding leaderboard Shortlist models for this job

Same basis rules as the category table below

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.

AutomationBenchAgentic
Normalized gap 26.2
Gemini 4 Carbon:53.9%
LFM2.5-24B-A2B:27.7%
Gemini 4 Carbon source LFM2.5-24B-A2B source

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.

Knowledge

Directional only

Gemini 4 Carbon: 88.2; Supported · #2/178
LFM2.5-24B-A2B: 39.3; Estimated · #118/178
Basis: BenchAlign v5.8 lane · 0 vs 3 public rows
Reading: Directional only

Agentic

Not comparable

Gemini 4 Carbon: Not ranked
LFM2.5-24B-A2B: 32.7; Estimated · #100/134
Basis: BenchAlign v5.8 lane · 4 vs 5 public rows
Reading: Not comparable

Coding

Not comparable

Gemini 4 Carbon: 82.2; Supported · #3/156
LFM2.5-24B-A2B: Not ranked
Basis: BenchAlign v5.8 lane · 1 vs 2 public rows
Reading: Not comparable

Reasoning

Not comparable

Gemini 4 Carbon: 79.5; Unranked · 2 rankable rows
LFM2.5-24B-A2B: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Multimodal

Not comparable

Gemini 4 Carbon: Not ranked
LFM2.5-24B-A2B: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Multilingual

Not comparable

Gemini 4 Carbon: Not ranked
LFM2.5-24B-A2B: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Instruction following

Not comparable

Gemini 4 Carbon: Not ranked
LFM2.5-24B-A2B: 75.6; #69/157
Basis: Provisional lane · 0 vs 1 weighted rows
Reading: Not comparable

Math

Not comparable

Gemini 4 Carbon: Not ranked
LFM2.5-24B-A2B: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for Gemini 4 Carbon and LFM2.5-24B-A2B
Category	Gemini 4 Carbon	LFM2.5-24B-A2B	Basis	Reading
Knowledge	88.2Supported · #2/178	39.3Estimated · #118/178	Directional onlyBenchAlign v5.8 lane · 0 vs 3 public rows	Directional only
Agentic	Not ranked	32.7Estimated · #100/134	Not comparableBenchAlign v5.8 lane · 4 vs 5 public rows	Not comparable
Coding	82.2Supported · #3/156	Not ranked	Not comparableBenchAlign v5.8 lane · 1 vs 2 public rows	Not comparable
Reasoning	79.5Unranked · 2 rankable rows	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Multimodal	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Multilingual	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Instruction following	Not ranked	75.6#69/157	Not comparableProvisional lane · 0 vs 1 weighted rows	Not comparable
Math	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	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
LFM2.5-24B-A2B: $0.0006; Fits in one request

LFM2.5-24B-A2B has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

Gemini 4 Carbon: $0.13; Fit state unavailable
LFM2.5-24B-A2B: $0.008; Fits in one request

LFM2.5-24B-A2B 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 4 Carbon: $0.16; Fit state unavailable
LFM2.5-24B-A2B: $0.022; Does not fit in one request

LFM2.5-24B-A2B does not fit this workload in one request.

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.

SpecificationGemini 4 CarbonLFM2.5-24B-A2B

Context window

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

Gemini 4 Carbon

Not sourced

Google Gemini 4 Carbon launch

LFM2.5-24B-A2B

131K

Liquid AI launch post

API model ID

Gemini 4 Carbon

Not sourced

LFM2.5-24B-A2B

Not sourced

Cached-input rate

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

Gemini 4 Carbon

$0.1 per 1M cached input tokens

Google Gemini 4 Carbon launch post

LFM2.5-24B-A2B

$0.05 per 1M cached input tokens

Liquid AI launch post

Documented inputs

Gemini 4 Carbon

Not sourced

LFM2.5-24B-A2B

Not sourced

Documented outputs

Gemini 4 Carbon

Not sourced

LFM2.5-24B-A2B

Not sourced

Provider availability

Gemini 4 Carbon

Limited Access · Fairwind Program

Google Gemini 4 Carbon launch

LFM2.5-24B-A2B

Not sourced

Reasoning profile

Gemini 4 Carbon

Reasoning

LFM2.5-24B-A2B

Reasoning

Weight access

Gemini 4 Carbon

Proprietary

LFM2.5-24B-A2B

Open Weight

License

Gemini 4 Carbon

Proprietary

LFM2.5-24B-A2B

Open Weight

Release date

Gemini 4 Carbon

2026-10-28

LFM2.5-24B-A2B

2026-10-13

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 4 Carbon has the higher public point estimate, 88.42 versus 36.97. Their conditional score ranges do not overlap. These ranges do not establish rank confidence.
Workload cost: Repository review: $0.13 vs $0.008. Cache-heavy agent loop: $0.16 vs $0.022.
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 LFM2.5-24B-A2B?

Gemini 4 Carbon has the higher public point estimate, 88.42 versus 36.97. Their conditional score ranges do not overlap. These ranges do not establish rank confidence. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, Gemini 4 Carbon or LFM2.5-24B-A2B?

LFM2.5-24B-A2B 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 LFM2.5-24B-A2B?

Gemini 4 Carbon is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, Gemini 4 Carbon or LFM2.5-24B-A2B?

For the stated presets, chat costs $0.007 on Gemini 4 Carbon and $0.0006 on LFM2.5-24B-A2B; repository review costs $0.13 and $0.008; the cache-heavy agent loop costs $0.16 and $0.022. LFM2.5-24B-A2B does not fit this workload in one request.

Which has the larger context window, Gemini 4 Carbon or LFM2.5-24B-A2B?

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.

LFM2.5-24B-A2B

API / mo$825

Self-host / mo$298

Break-even27M/day

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 evidence21 rows

Agentic

AutomationBench
Gemini 4 Carbon53.9%
Source
LFM2.5-24B-A2B27.7%
Source
Gemini 4 Carbon leads this result
OSWorld 2.1 Offline
Gemini 4 Carbon81.5%
Source
LFM2.5-24B-A2B—
Not directly comparable
CWE-bench v1
Gemini 4 Carbon71.0%
Source
LFM2.5-24B-A2B—
Not directly comparable
ExploitGym
Gemini 4 Carbon53.5%
Source
LFM2.5-24B-A2B—
Not directly comparable
BFCL v4
Gemini 4 Carbon—
LFM2.5-24B-A2B65.0%
Source
Not directly comparable
τ³-bench results
Gemini 4 Carbon—
LFM2.5-24B-A2B8.0%
Source
Not directly comparable
PinchBench
Gemini 4 Carbon—
LFM2.5-24B-A2B83.2%
Source
Not directly comparable
WebArena-Verified
Gemini 4 Carbon—
LFM2.5-24B-A2B26.8%
Source
Not directly comparable

Coding

FrontierSWE v2
Gemini 4 Carbon61.2%
Source
LFM2.5-24B-A2B—
Not directly comparable
SciCode
Gemini 4 Carbon—
LFM2.5-24B-A2B32.7%
Source
Not directly comparable
SWE-bench Verified
Gemini 4 Carbon—
LFM2.5-24B-A2B51%
Source
Not directly comparable

Reasoning

GraphWalks BFS 256K–1M
Gemini 4 Carbon96.2%
Source
LFM2.5-24B-A2B—
Not directly comparable

Multimodal

Chartography (no tools)
Gemini 4 Carbon72.5%
Source
LFM2.5-24B-A2B—
Not directly comparable
Blueprint-Bench 2
Gemini 4 Carbon58.6%
Source
LFM2.5-24B-A2B—
Not directly comparable

Knowledge

GPQA
Gemini 4 Carbon—
LFM2.5-24B-A2B74.4%
Source
Not directly comparable
GPQA-D
Gemini 4 Carbon—
LFM2.5-24B-A2B74.4%
Source
Not directly comparable
HLE
Gemini 4 Carbon—
LFM2.5-24B-A2B7.4%
Source
Not directly comparable

Instruction following

Gray Swan IPI (15 attempts)
Gemini 4 Carbon0.0%
Source
LFM2.5-24B-A2B—
Not directly comparable
IFEval
Gemini 4 Carbon—
LFM2.5-24B-A2B96.8%
Source
Not directly comparable
IFBench
Gemini 4 Carbon—
LFM2.5-24B-A2B74.7%
Source
Not directly comparable

Math

MATH-500
Gemini 4 Carbon—
LFM2.5-24B-A2B96.0%
Source
Not directly comparable

21 public results · 1 shared

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Last updated October 28, 2026

Gemini 4 Carbon vs LFM2.5-24B-A2B

Decision reading

Which one for your work

Chat turn cost

Repository review cost

Coding work

Agentic work

Long documents

Cache-heavy agent loop cost

Which one for a specific job

What is actually comparable

Shape of the matched evidence

Category results, on a stated basis

Knowledge

Agentic

Coding

Reasoning

Multimodal

Multilingual

Instruction following

Math

What each workload costs

Chat turn

Repository review

Cache-heavy agent loop

Specification differences

Context window

API model ID

Cached-input rate

Documented inputs

Documented outputs

Provider availability

Reasoning profile

Weight access

License

Release date

If you already use one of these models

Questions

Which is better, Gemini 4 Carbon or LFM2.5-24B-A2B?

Which is better for coding, Gemini 4 Carbon or LFM2.5-24B-A2B?

Which is better for agentic tasks, Gemini 4 Carbon or LFM2.5-24B-A2B?

Which costs less, Gemini 4 Carbon or LFM2.5-24B-A2B?

Which has the larger context window, Gemini 4 Carbon or LFM2.5-24B-A2B?

Self-host vs API cost

Benchmark evidence

Agentic

Coding

Reasoning

Multimodal

Knowledge

Instruction following

Math

Related comparisons

Watch Gemini 4 Carbon vs LFM2.5-24B-A2B