Long documents
Prompts that approach the documented context limit
Laguna XS 2.1
Laguna XS 2.1 has the larger documented context window.
Confidence: documented
Laguna XS 2.1 vs LFM2.5-24B-A2B
Updated October 13, 2026. We do not rank this pair: at least one has no public score. 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
Share or export
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.
- Shared results
- 1
- Laguna XS 2.1 only
- 4
- LFM2.5-24B-A2B only
- 12
- Like-for-like categories
- 0 / 8
Estimated: 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
Laguna XS 2.1
Laguna XS 2.1 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
Laguna XS 2.1
Laguna XS 2.1 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
Laguna XS 2.1 is not ranked on the public lane for coding, so no winner is named for coding.
Confidence: limitedAgentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Laguna XS 2.1 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.
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.
—Laguna XS 2.128.5LFM2.5-24B-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.
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.
4 Laguna XS 2.1 only
1 Shared
12 LFM2.5-24B-A2B only
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.
SWE-bench VerifiedCoding
Normalized gap 19.9Laguna XS 2.1:70.9%LFM2.5-24B-A2B:51%
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- Laguna XS 2.1
- Not ranked
- LFM2.5-24B-A2B
- 30.2
- Estimated · #88/124
- Basis
- BenchAlign v5.8 lane · 1 vs 5 public rows
- Reading
- Not comparable
Coding
Not comparable- Laguna XS 2.1
- Not ranked
- LFM2.5-24B-A2B
- 28.5
- Estimated · #98/149
- Basis
- BenchAlign v5.8 lane · 4 vs 2 public rows
- Reading
- Not comparable
Reasoning
Not comparable- Laguna XS 2.1
- Not ranked
- LFM2.5-24B-A2B
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Laguna XS 2.1
- Not ranked
- LFM2.5-24B-A2B
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- Laguna XS 2.1
- Not ranked
- LFM2.5-24B-A2B
- 40.8
- Estimated · #112/181
- Basis
- BenchAlign v5.8 lane · 0 vs 3 public rows
- Reading
- Not comparable
Multilingual
Not comparable- Laguna XS 2.1
- Not ranked
- LFM2.5-24B-A2B
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Laguna XS 2.1
- Not ranked
- LFM2.5-24B-A2B
- 75.6
- #61/128
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Not comparable
Math
Not comparable- Laguna XS 2.1
- Not ranked
- LFM2.5-24B-A2B
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | Laguna XS 2.1 | LFM2.5-24B-A2B | Basis | Reading |
|---|---|---|---|---|
| Agentic | Not ranked | 30.2Estimated · #88/124 | Not comparableBenchAlign v5.8 lane · 1 vs 5 public rows | Not comparable |
| Coding | Not ranked | 28.5Estimated · #98/149 | Not comparableBenchAlign v5.8 lane · 4 vs 2 public rows | Not comparable |
| Reasoning | Not ranked | 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 |
| Knowledge | Not ranked | 40.8Estimated · #112/181 | Not comparableBenchAlign v5.8 lane · 0 vs 3 public rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | Not ranked | 75.6#61/128 | 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.
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
- Laguna XS 2.1
- $0.0002
- Fits in one request
- LFM2.5-24B-A2B
- $0.0006
- Fits in one request
Laguna XS 2.1 has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- Laguna XS 2.1
- $0.0056
- Fits in one request
- LFM2.5-24B-A2B
- $0.008
- Fits in one request
Laguna XS 2.1 has the lower modeled cost
Costs use the listed standard API rates.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- Laguna XS 2.1
- $0.014
- Fits in one request
- 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.
SpecificationLaguna XS 2.1LFM2.5-24B-A2B
Context window
Maximum documented context; output-token limits may be lower.
Laguna XS 2.1
LFM2.5-24B-A2B
API model ID
Laguna XS 2.1
poolside/Laguna-XS-2.1
Poolside Laguna XS 2.1 launch postLFM2.5-24B-A2B
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Laguna XS 2.1
$0.05 per 1M cached input tokens
Poolside Laguna XS 2.1 launch postLFM2.5-24B-A2B
$0.05 per 1M cached input tokens
Liquid AI launch postDocumented inputs
Laguna XS 2.1
Not sourced
LFM2.5-24B-A2B
Not sourced
Documented outputs
Laguna XS 2.1
Not sourced
LFM2.5-24B-A2B
Not sourced
Provider availability
Laguna XS 2.1
Not sourced
LFM2.5-24B-A2B
Not sourced
Reasoning profile
Laguna XS 2.1
Reasoning
LFM2.5-24B-A2B
Reasoning
Weight access
Laguna XS 2.1
Open Weight
LFM2.5-24B-A2B
Open Weight
License
Laguna XS 2.1
Open Weight
LFM2.5-24B-A2B
Open Weight
Release date
Laguna XS 2.1
2026-07-02
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
- 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
- Repository review: $0.0056 vs $0.008. Cache-heavy agent loop: $0.014 vs $0.022.
- Context tradeoff
- Laguna XS 2.1 has the larger documented window (262K).
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, Laguna XS 2.1 or LFM2.5-24B-A2B?
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, Laguna XS 2.1 or LFM2.5-24B-A2B?
Laguna XS 2.1 is not ranked on the public lane for coding, so no winner is named for coding.
Which is better for agentic tasks, Laguna XS 2.1 or LFM2.5-24B-A2B?
Laguna XS 2.1 is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, Laguna XS 2.1 or LFM2.5-24B-A2B?
For the stated presets, chat costs $0.0002 on Laguna XS 2.1 and $0.0006 on LFM2.5-24B-A2B; repository review costs $0.0056 and $0.008; the cache-heavy agent loop costs $0.014 and $0.022. LFM2.5-24B-A2B does not fit this workload in one request.
Which has the larger context window, Laguna XS 2.1 or LFM2.5-24B-A2B?
Laguna XS 2.1 has the larger documented context window: 262K, 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 evidence17 rows
Agentic
Terminal-Bench 2.0
Laguna XS 2.137.5%SourceLFM2.5-24B-A2B—Not directly comparable
AutomationBench
Laguna XS 2.1—LFM2.5-24B-A2B27.7%SourceNot directly comparable
BFCL v4
Laguna XS 2.1—LFM2.5-24B-A2B65.0%SourceNot directly comparable
τ³-bench results
Laguna XS 2.1—LFM2.5-24B-A2B8.0%SourceNot directly comparable
PinchBench
Laguna XS 2.1—LFM2.5-24B-A2B83.2%SourceNot directly comparable
WebArena-Verified
Laguna XS 2.1—LFM2.5-24B-A2B26.8%SourceNot directly comparable
Coding
SWE-bench Verified
Laguna XS 2.170.9%SourceLFM2.5-24B-A2B51%SourceLaguna XS 2.1 leads this result
SWE Multilingual
Laguna XS 2.163.1%SourceLFM2.5-24B-A2B—Not directly comparable
SWE-bench Pro
Laguna XS 2.147.6%SourceLFM2.5-24B-A2B—Not directly comparable
Terminal-Bench 2.0
Laguna XS 2.137.5%SourceLFM2.5-24B-A2B—Not directly comparable
SciCode
Laguna XS 2.1—LFM2.5-24B-A2B32.7%SourceNot directly comparable
Knowledge
Instruction following
Math
MATH-500
Laguna XS 2.1—LFM2.5-24B-A2B96.0%SourceNot directly comparable
17 public results · 1 shared
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Last updated October 13, 2026