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Data

Grok Code Fast 1 vs LFM2.5-24B-A2B

Updated October 13, 2026. Rank says LFM2.5-24B-A2B is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

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Decision reading

LFM2.5-24B-A2B has the higher public point estimate, 37.8 versus 33.97. Their conditional score ranges 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
xAI logo

xAI

33.97/100

Estimated · Public rank #159

Conditional range 24.3–43.7

Model B
LiquidAI logo

LiquidAI

37.8/100

Estimated · Public rank #141

Conditional range 23.4–52.1

Shared results
1
Grok Code Fast 1 only
1
LFM2.5-24B-A2B only
12
Like-for-like categories
0 / 8
Estimated: Grok Code Fast 1 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.

  • Long documents

    Prompts that approach the documented context limit

    Grok Code Fast 1

    Grok Code Fast 1 has the larger documented context window.

    Confidence: documented
  • 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

    Grok Code Fast 1 and LFM2.5-24B-A2B are scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.

    Confidence: limited
  • Agentic work

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

    Not enough matched evidence

    Grok Code Fast 1 is not ranked on the public lane for agentic, so no winner is named for agentic.

    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. Grok Code Fast 1 has no published cached-input rate, so cached tokens use its listed input rate.

    Confidence: rate-fallback

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.

17.0Grok Code Fast 128.5LFM2.5-24B-A2B

Directional only · BenchAlign v5.8

LFM2.5-24B-A2B has the higher coding point estimate. Conditional score ranges do not establish rank confidence.

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.

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.

Coding

Directional only
Grok Code Fast 1
17.0
Estimated · #127/149
LFM2.5-24B-A2B
28.5
Estimated · #98/149
Basis
BenchAlign v5.8 lane · 2 vs 2 public rows
Reading
Directional only

Knowledge

Directional only
Grok Code Fast 1
36.0
Estimated · #129/181
LFM2.5-24B-A2B
40.8
Estimated · #112/181
Basis
BenchAlign v5.8 lane · 0 vs 3 public rows
Reading
Directional only

Instruction following

Directional only
Grok Code Fast 1
48.2
#88/128
LFM2.5-24B-A2B
75.6
#61/128
Basis
Provisional lane · 0 vs 1 weighted rows
Reading
Directional only

Agentic

Not comparable
Grok Code Fast 1
Not ranked
LFM2.5-24B-A2B
30.2
Estimated · #88/124
Basis
BenchAlign v5.8 lane · 0 vs 5 public rows
Reading
Not comparable

Reasoning

Not comparable
Grok Code Fast 1
62.3
Unranked · 2 rankable rows
LFM2.5-24B-A2B
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
Grok Code Fast 1
Not ranked
LFM2.5-24B-A2B
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multilingual

Not comparable
Grok Code Fast 1
Not ranked
LFM2.5-24B-A2B
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
Grok Code Fast 1
Not ranked
LFM2.5-24B-A2B
Not ranked
Basis
Provisional lane · 0 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 v5.8) 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.

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

Grok Code Fast 1
$0.00095
Fits in one request
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

Grok Code Fast 1
$0.0145
Fits in one request
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

Grok Code Fast 1
$0.059
Fits in one request
Cached input priced at the published list-input rate
LFM2.5-24B-A2B
$0.022
Does not fit in one request

LFM2.5-24B-A2B does not fit this workload in one request. Grok Code Fast 1 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.

Grok Code Fast 1

256K

LFM2.5-24B-A2B

API model ID

Grok Code Fast 1

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.

Grok Code Fast 1

Not published

LFM2.5-24B-A2B

$0.05 per 1M cached input tokens

Liquid AI launch post

Documented inputs

Grok Code Fast 1

Not sourced

LFM2.5-24B-A2B

Not sourced

Documented outputs

Grok Code Fast 1

Not sourced

LFM2.5-24B-A2B

Not sourced

Provider availability

Grok Code Fast 1

Not sourced

LFM2.5-24B-A2B

Not sourced

Reasoning profile

Grok Code Fast 1

Non-Reasoning

LFM2.5-24B-A2B

Reasoning

Weight access

Grok Code Fast 1

Proprietary

LFM2.5-24B-A2B

Open Weight

License

Grok Code Fast 1

Proprietary

LFM2.5-24B-A2B

Open Weight

Release date

Grok Code Fast 1

2025-08-28

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
LFM2.5-24B-A2B has the higher public point estimate, 37.8 versus 33.97. Their conditional score ranges overlap. These ranges do not establish rank confidence.
Workload cost
Repository review: $0.0145 vs $0.008. Cache-heavy agent loop: $0.059 vs $0.022.
Context tradeoff
Grok Code Fast 1 has the larger documented window (256K).
Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, Grok Code Fast 1 or LFM2.5-24B-A2B?

LFM2.5-24B-A2B has the higher public point estimate, 37.8 versus 33.97. Their conditional score ranges 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, Grok Code Fast 1 or LFM2.5-24B-A2B?

LFM2.5-24B-A2B scores higher for coding on the public lane, 28.5 to 17. Grok Code Fast 1 and LFM2.5-24B-A2B are scored on Estimated evidence for coding, so the reading is directional rather than like-for-like. BenchLM does not name a winner for coding from a directional reading; read the shared benchmark rows directly and test the models on the same task set.

Which is better for agentic tasks, Grok Code Fast 1 or LFM2.5-24B-A2B?

Grok Code Fast 1 is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, Grok Code Fast 1 or LFM2.5-24B-A2B?

For the stated presets, chat costs $0.00095 on Grok Code Fast 1 and $0.0006 on LFM2.5-24B-A2B; repository review costs $0.0145 and $0.008; the cache-heavy agent loop costs $0.059 and $0.022. LFM2.5-24B-A2B does not fit this workload in one request. Grok Code Fast 1 has no published cached-input rate, so cached tokens use its listed input rate.

Which has the larger context window, Grok Code Fast 1 or LFM2.5-24B-A2B?

Grok Code Fast 1 has the larger documented context window: 256K, compared with 131K.

Benchmark evidence

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

Browse raw public benchmark evidence14 rows

Agentic

  • AutomationBench

    Grok Code Fast 1—
    LFM2.5-24B-A2B27.7%
    Source

    Not directly comparable

  • BFCL v4

    Grok Code Fast 1—
    LFM2.5-24B-A2B65.0%
    Source

    Not directly comparable

  • τ³-bench results

    Grok Code Fast 1—
    LFM2.5-24B-A2B8.0%
    Source

    Not directly comparable

  • PinchBench

    Grok Code Fast 1—
    LFM2.5-24B-A2B83.2%
    Source

    Not directly comparable

  • WebArena-Verified

    Grok Code Fast 1—
    LFM2.5-24B-A2B26.8%
    Source

    Not directly comparable

Coding

  • SWE-bench Verified

    Grok Code Fast 170.8%
    Source
    LFM2.5-24B-A2B51%
    Source

    Grok Code Fast 1 leads this result

  • LiveCodeBench (Vals)

    Grok Code Fast 162.0%
    Source
    LFM2.5-24B-A2B—

    Not directly comparable

  • SciCode

    Grok Code Fast 1—
    LFM2.5-24B-A2B32.7%
    Source

    Not directly comparable

Knowledge

  • GPQA

    Grok Code Fast 1—
    LFM2.5-24B-A2B74.4%
    Source

    Not directly comparable

  • GPQA-D

    Grok Code Fast 1—
    LFM2.5-24B-A2B74.4%
    Source

    Not directly comparable

  • HLE

    Grok Code Fast 1—
    LFM2.5-24B-A2B7.4%
    Source

    Not directly comparable

Instruction following

  • IFEval

    Grok Code Fast 1—
    LFM2.5-24B-A2B96.8%
    Source

    Not directly comparable

  • IFBench

    Grok Code Fast 1—
    LFM2.5-24B-A2B74.7%
    Source

    Not directly comparable

Math

  • MATH-500

    Grok Code Fast 1—
    LFM2.5-24B-A2B96.0%
    Source

    Not directly comparable

14 public results · 1 shared

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Last updated October 13, 2026