Long documents
Prompts that approach the documented context limit
GPT-4.1 nano
GPT-4.1 nano has the larger documented context window.
Confidence: documented
GPT-4.1 nano 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.
Both of these models will change. Get the price, version and retirement notices for the pair, each with its source. Follow model changes
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Decision reading
LFM2.5-24B-A2B has the higher public point estimate, 37.8 versus 24.41. Their conditional score ranges overlap. These ranges do not establish rank confidence. 2 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 2
- GPT-4.1 nano only
- 1
- LFM2.5-24B-A2B only
- 11
- Like-for-like categories
- 0 / 8
Estimated: GPT-4.1 nano 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
GPT-4.1 nano
GPT-4.1 nano 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
GPT-4.1 nano
GPT-4.1 nano has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-ratesCoding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
GPT-4.1 nano and LFM2.5-24B-A2B are scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.
Confidence: limitedAgentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
GPT-4.1 nano is not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limitedCache-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. GPT-4.1 nano 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.
10.7GPT-4.1 nano28.5LFM2.5-24B-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.
1 GPT-4.1 nano only
2 Shared
11 LFM2.5-24B-A2B only
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.
GPQAKnowledge
Normalized gap 24.1GPT-4.1 nano:50.3%LFM2.5-24B-A2B:74.4%
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- GPT-4.1 nano
- 10.7
- Estimated · #142/149
- LFM2.5-24B-A2B
- 28.5
- Estimated · #98/149
- Basis
- BenchAlign v5.8 lane · 0 vs 2 public rows
- Reading
- Directional only
Knowledge
Directional only- GPT-4.1 nano
- 25.7
- Supported · #168/181
- LFM2.5-24B-A2B
- 40.8
- Estimated · #112/181
- Basis
- BenchAlign v5.8 lane · 2 vs 3 public rows
- Reading
- Directional only
Instruction following
Directional only- GPT-4.1 nano
- 36.0
- #111/128
- LFM2.5-24B-A2B
- 75.6
- #61/128
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Directional only
Agentic
Not comparable- GPT-4.1 nano
- 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- GPT-4.1 nano
- 39.8
- Unranked · 2 rankable rows
- LFM2.5-24B-A2B
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- GPT-4.1 nano
- 29.8
- Unranked · 1 rankable row
- LFM2.5-24B-A2B
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- GPT-4.1 nano
- Not ranked
- LFM2.5-24B-A2B
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- GPT-4.1 nano
- Not ranked
- LFM2.5-24B-A2B
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | GPT-4.1 nano | LFM2.5-24B-A2B | Basis | Reading |
|---|---|---|---|---|
| Coding | 10.7Estimated · #142/149 | 28.5Estimated · #98/149 | Directional onlyBenchAlign v5.8 lane · 0 vs 2 public rows | Directional only |
| Knowledge | 25.7Supported · #168/181 | 40.8Estimated · #112/181 | Directional onlyBenchAlign v5.8 lane · 2 vs 3 public rows | Directional only |
| Instruction following | 36.0#111/128 | 75.6#61/128 | Directional onlyProvisional lane · 0 vs 1 weighted rows | Directional only |
| Agentic | Not ranked | 30.2Estimated · #88/124 | Not comparableBenchAlign v5.8 lane · 0 vs 5 public rows | Not comparable |
| Reasoning | 39.8Unranked · 2 rankable rows | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | 29.8Unranked · 1 rankable row | 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 |
| 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
- GPT-4.1 nano
- $0.0003
- Fits in one request
- LFM2.5-24B-A2B
- $0.0006
- Fits in one request
GPT-4.1 nano has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- GPT-4.1 nano
- $0.0062
- Fits in one request
- LFM2.5-24B-A2B
- $0.008
- Fits in one request
GPT-4.1 nano has the lower modeled cost
Costs use the listed standard API rates.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- GPT-4.1 nano
- $0.026
- 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. GPT-4.1 nano 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.
SpecificationGPT-4.1 nanoLFM2.5-24B-A2B
Context window
Maximum documented context; output-token limits may be lower.
GPT-4.1 nano
1M
LFM2.5-24B-A2B
API model ID
GPT-4.1 nano
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.
GPT-4.1 nano
Not published
LFM2.5-24B-A2B
$0.05 per 1M cached input tokens
Liquid AI launch postDocumented inputs
GPT-4.1 nano
Not sourced
LFM2.5-24B-A2B
Not sourced
Documented outputs
GPT-4.1 nano
Not sourced
LFM2.5-24B-A2B
Not sourced
Provider availability
GPT-4.1 nano
Not sourced
LFM2.5-24B-A2B
Not sourced
Reasoning profile
GPT-4.1 nano
Non-Reasoning
LFM2.5-24B-A2B
Reasoning
Weight access
GPT-4.1 nano
Proprietary
LFM2.5-24B-A2B
Open Weight
License
GPT-4.1 nano
Proprietary
LFM2.5-24B-A2B
Open Weight
Release date
GPT-4.1 nano
2025-04-14
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 24.41. Their conditional score ranges overlap. These ranges do not establish rank confidence.
- Workload cost
- Repository review: $0.0062 vs $0.008. Cache-heavy agent loop: $0.026 vs $0.022.
- Context tradeoff
- GPT-4.1 nano has the larger documented window (1M).
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, GPT-4.1 nano or LFM2.5-24B-A2B?
LFM2.5-24B-A2B has the higher public point estimate, 37.8 versus 24.41. 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, GPT-4.1 nano or LFM2.5-24B-A2B?
LFM2.5-24B-A2B scores higher for coding on the public lane, 28.5 to 10.7. GPT-4.1 nano 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, GPT-4.1 nano or LFM2.5-24B-A2B?
GPT-4.1 nano is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, GPT-4.1 nano or LFM2.5-24B-A2B?
For the stated presets, chat costs $0.0003 on GPT-4.1 nano and $0.0006 on LFM2.5-24B-A2B; repository review costs $0.0062 and $0.008; the cache-heavy agent loop costs $0.026 and $0.022. LFM2.5-24B-A2B does not fit this workload in one request. GPT-4.1 nano has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, GPT-4.1 nano or LFM2.5-24B-A2B?
GPT-4.1 nano has the larger documented context window: 1M, 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
GPT-4.1 nano—LFM2.5-24B-A2B27.7%SourceNot directly comparable
BFCL v4
GPT-4.1 nano—LFM2.5-24B-A2B65.0%SourceNot directly comparable
τ³-bench results
GPT-4.1 nano—LFM2.5-24B-A2B8.0%SourceNot directly comparable
PinchBench
GPT-4.1 nano—LFM2.5-24B-A2B83.2%SourceNot directly comparable
WebArena-Verified
GPT-4.1 nano—LFM2.5-24B-A2B26.8%SourceNot directly comparable
Coding
Knowledge
Instruction following
Math
MATH-500
GPT-4.1 nano—LFM2.5-24B-A2B96.0%SourceNot directly comparable
14 public results · 2 shared
Watch GPT-4.1 nano vs LFM2.5-24B-A2B
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Last updated October 13, 2026