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BenchLM

LFM2-24B-A2B vs Ministral 3 8B

Updated September 23, 2026. Rank says LFM2-24B-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

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 resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A
LiquidAI logo

LiquidAI

19.86/100

Estimated · Public rank #186

90% interval 14.125.6

Model B
Mistral logo

Mistral

19.25/100

Estimated · Public rank #187

90% interval 13.525.0

Shared results
0
LFM2-24B-A2B only
0
Ministral 3 8B only
0
Like-for-like categories
0 / 8
Estimated: LFM2-24B-A2B and Ministral 3 8BHow 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

    Ministral 3 8B

    Ministral 3 8B has the larger documented context window.

    Confidence: documented
Show secondary and unsupported calls
  • Coding work

    Code generation, repair, and software-engineering tasks

    Not enough matched evidence

    LFM2-24B-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

    LFM2-24B-A2B is not ranked on the public lane for agentic, so no winner is named for agentic.

    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

    The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. LFM2-24B-A2B does not fit this workload in one request. Ministral 3 8B has no published cached-input rate, so cached tokens use its listed input rate. LFM2-24B-A2B has no comparable published API token rate.

    Confidence: rate-fallback
  • Repository review cost

    50K fresh input + 3K 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. LFM2-24B-A2B does not fit this workload in one request. LFM2-24B-A2B has no comparable published API token rate.

    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.

LFM2-24B-A2B23.5Ministral 3 8B

Not comparable · BenchAlign v5.6

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.

Evidence parity totals are not available.

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 v5.6 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
LFM2-24B-A2B
Not ranked
Ministral 3 8B
13.6
Estimated · #98/105
Basis
BenchAlign v5.6 lane · 0 vs 0 public rows
Reading
Not comparable

Coding

Not comparable
LFM2-24B-A2B
Not ranked
Ministral 3 8B
23.5
Estimated · #119/135
Basis
BenchAlign v5.6 lane · 0 vs 0 public rows
Reading
Not comparable

Reasoning

Not comparable
LFM2-24B-A2B
Not ranked
Ministral 3 8B
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
LFM2-24B-A2B
Not ranked
Ministral 3 8B
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Knowledge

Not comparable
LFM2-24B-A2B
Not ranked
Ministral 3 8B
Not ranked
Basis
BenchAlign v5.6 lane · 0 vs 0 public rows
Reading
Not comparable

Multilingual

Not comparable
LFM2-24B-A2B
Not ranked
Ministral 3 8B
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
LFM2-24B-A2B
Not ranked
Ministral 3 8B
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
LFM2-24B-A2B
Not ranked
Ministral 3 8B
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 v5.6) 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

LFM2-24B-A2B
No hosted API token rate
Fits in one request
Ministral 3 8B
$0.00022
Fits in one request

LFM2-24B-A2B has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

LFM2-24B-A2B
No hosted API token rate
Does not fit in one request
Ministral 3 8B
$0.00795
Fits in one request

LFM2-24B-A2B does not fit this workload in one request. LFM2-24B-A2B has no comparable published API token rate.

Cache-heavy agent loop

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

LFM2-24B-A2B
No hosted API token rate
Does not fit in one request
Cached-input rate unavailable
Ministral 3 8B
$0.0345
Fits in one request
Cached input priced at the published list-input rate

LFM2-24B-A2B does not fit this workload in one request. Ministral 3 8B has no published cached-input rate, so cached tokens use its listed input rate. LFM2-24B-A2B 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.

Context window

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

LFM2-24B-A2B

32K

Ministral 3 8B

256K

API model ID

LFM2-24B-A2B

Not sourced

Ministral 3 8B

Not sourced

Cached-input rate

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

LFM2-24B-A2B

No comparable hosted API rate

Ministral 3 8B

Not published

Documented inputs

LFM2-24B-A2B

Not sourced

Ministral 3 8B

Not sourced

Documented outputs

LFM2-24B-A2B

Not sourced

Ministral 3 8B

Not sourced

Provider availability

LFM2-24B-A2B

Not sourced

Ministral 3 8B

Not sourced

Reasoning profile

LFM2-24B-A2B

Non-Reasoning

Ministral 3 8B

Non-Reasoning

Weight access

LFM2-24B-A2B

Proprietary

Ministral 3 8B

Open Weight

License

LFM2-24B-A2B

Proprietary

Ministral 3 8B

Open Weight

Release date

LFM2-24B-A2B

2026-01-10

Ministral 3 8B

2025-12-02

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
A complete comparable API-rate estimate is not available for both models.
Context tradeoff
Ministral 3 8B has the larger documented window (256K).
Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, LFM2-24B-A2B or Ministral 3 8B?

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, LFM2-24B-A2B or Ministral 3 8B?

LFM2-24B-A2B is not ranked on the public lane for coding, so no winner is named for coding.

Which is better for agentic tasks, LFM2-24B-A2B or Ministral 3 8B?

LFM2-24B-A2B is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, LFM2-24B-A2B or Ministral 3 8B?

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, LFM2-24B-A2B or Ministral 3 8B?

Ministral 3 8B has the larger documented context window: 256K, compared with 32K.

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Last updated September 23, 2026