LFM2.5-2.6B vs Muse Spark 1.1

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

LFM2.5-2.6B

LiquidAI

39.9/100

Estimated · Public rank #190

90% interval 28.4–51.4

Model B

Muse Spark 1.1

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
Muse Spark 1.1
Muse Spark 1.1 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
LFM2.5-2.6B is 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
LFM2.5-2.6B is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
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. LFM2.5-2.6B does not fit this workload in one request. LFM2.5-2.6B has no comparable published API token rate. Muse Spark 1.1 has no comparable published API token rate.
Confidence: listed-rates
Repository review cost
50K fresh input + 3K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
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.

39.2LFM2.5-2.6B59.5Muse Spark 1.1

Directional only · BenchAlign

Muse Spark 1.1 scores higher, but at least one score rests on Estimated evidence or a different benchmark set. Directional only, no winner.

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

What is actually comparable

Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.

7 LFM2.5-2.6B only

26 Muse Spark 1.1 only

Shared results: 0
LFM2.5-2.6B only: 7
Muse Spark 1.1 only: 26
Like-for-like categories: 0 / 8

3 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign 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

Directional only

LFM2.5-2.6B: 37.2; Estimated · #131/154
Muse Spark 1.1: 60.0; Supported · #21/154
Basis: BenchAlign lane · 4 vs 14 public rows
Reading: Directional only

Coding

Directional only

LFM2.5-2.6B: 39.2; Estimated · #125/154
Muse Spark 1.1: 59.5; Supported · #23/154
Basis: BenchAlign lane · 1 vs 4 public rows
Reading: Directional only

Knowledge

Directional only

LFM2.5-2.6B: 37.0; Estimated · #151/184
Muse Spark 1.1: 70.6; Supported · #11/184
Basis: BenchAlign lane · 0 vs 5 public rows
Reading: Directional only

Reasoning

Not comparable

LFM2.5-2.6B: 24.9; Unranked · 2 rankable rows
Muse Spark 1.1: 74.4; Unranked · 3 rankable rows
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

LFM2.5-2.6B: Not ranked
Muse Spark 1.1: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Multilingual

Not comparable

LFM2.5-2.6B: Not ranked
Muse Spark 1.1: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Multimodal

Not comparable

LFM2.5-2.6B: Not ranked
Muse Spark 1.1: 77.3; Unranked · 2 rankable rows
Basis: Provisional lane · 0 vs 1 weighted rows
Reading: Not comparable

Instruction following

Not comparable

LFM2.5-2.6B: 38.1; #104/124
Muse Spark 1.1: Not ranked
Basis: Provisional lane · 1 vs 0 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for LFM2.5-2.6B and Muse Spark 1.1
Category	LFM2.5-2.6B	Muse Spark 1.1	Basis	Reading
Agentic	37.2Estimated · #131/154	60.0Supported · #21/154	Directional onlyBenchAlign lane · 4 vs 14 public rows	Directional only
Coding	39.2Estimated · #125/154	59.5Supported · #23/154	Directional onlyBenchAlign lane · 1 vs 4 public rows	Directional only
Knowledge	37.0Estimated · #151/184	70.6Supported · #11/184	Directional onlyBenchAlign lane · 0 vs 5 public rows	Directional only
Reasoning	24.9Unranked · 2 rankable rows	74.4Unranked · 3 rankable rows	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
Multilingual	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Multimodal	Not ranked	77.3Unranked · 2 rankable rows	Not comparableProvisional lane · 0 vs 1 weighted rows	Not comparable
Instruction following	38.1#104/124	Not ranked	Not comparableProvisional lane · 1 vs 0 weighted rows	Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign) differ from the provisional-lane categories. Unranked scores sit on the lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.

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.

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

LFM2.5-2.6B: Self-hosted; infrastructure cost varies; Fits in one request
Muse Spark 1.1: API rate not published; Fits in one request

LFM2.5-2.6B has no comparable published API token rate. Muse Spark 1.1 has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

LFM2.5-2.6B: Self-hosted; infrastructure cost varies; Fits in one request
Muse Spark 1.1: API rate not published; Fits in one request

LFM2.5-2.6B has no comparable published API token rate. Muse Spark 1.1 has no comparable published API token rate.

Cache-heavy agent loop

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

LFM2.5-2.6B: Self-hosted; infrastructure cost varies; Does not fit in one request; Cached-input rate unavailable
Muse Spark 1.1: API rate not published; Fits in one request; Cached-input rate unavailable

LFM2.5-2.6B does not fit this workload in one request. LFM2.5-2.6B has no comparable published API token rate. Muse Spark 1.1 has no comparable published API token rate.

Specification differences

Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.

SpecificationLFM2.5-2.6BMuse Spark 1.1

Context window

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

LFM2.5-2.6B

128K

LiquidAI Hugging Face model card

Muse Spark 1.1

API model ID

LFM2.5-2.6B

Not sourced

Muse Spark 1.1

Not sourced

Cached-input rate

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

LFM2.5-2.6B

No comparable hosted API rate

LiquidAI Hugging Face model card

Muse Spark 1.1

No comparable hosted API rate

Documented inputs

LFM2.5-2.6B

Not sourced

Muse Spark 1.1

Not sourced

Documented outputs

LFM2.5-2.6B

Not sourced

Muse Spark 1.1

Not sourced

Provider availability

LFM2.5-2.6B

Not sourced

Muse Spark 1.1

Not sourced

Reasoning profile

LFM2.5-2.6B

Reasoning

Muse Spark 1.1

Reasoning

Weight access

LFM2.5-2.6B

Open Weight

Muse Spark 1.1

Proprietary

License

LFM2.5-2.6B

Open Weight

Muse Spark 1.1

Proprietary

Release date

LFM2.5-2.6B

2026-08-04

Muse Spark 1.1

2026-07-09

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: Muse Spark 1.1 has the larger documented window (1M).

Run the same representative tasks against both endpoints before changing production traffic.

Benchmark evidence

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

Browse raw public benchmark evidence33 rows

Agentic

BFCL v4
LFM2.5-2.6B56.9%
Source
Muse Spark 1.1—
Not directly comparable
τ³-bench results
LFM2.5-2.6B5.7%
Source
Muse Spark 1.1—
Not directly comparable
Claw-Eval
LFM2.5-2.6B62.9%
Source
Muse Spark 1.1—
Not directly comparable
PinchBench
LFM2.5-2.6B68.2%
Source
Muse Spark 1.1—
Not directly comparable
Terminal-Bench 2.0
LFM2.5-2.6B—
Muse Spark 1.180%
Source
Not directly comparable
MCP Atlas
LFM2.5-2.6B—
Muse Spark 1.188.1%
Source
Not directly comparable
Toolathlon
LFM2.5-2.6B—
Muse Spark 1.175.6%
Source
Not directly comparable
OSWorld-Verified
LFM2.5-2.6B—
Muse Spark 1.180.8%
Source
Not directly comparable
WebArena-Verified
LFM2.5-2.6B—
Muse Spark 1.169%
Source
Not directly comparable
DeepSearchQA
LFM2.5-2.6B—
Muse Spark 1.184.9%
Source
Not directly comparable
CyberGym
LFM2.5-2.6B—
Muse Spark 1.159.0%
Source
Not directly comparable
Finance Agent v2
LFM2.5-2.6B—
Muse Spark 1.157.2%
Source
Not directly comparable
deepSwe
LFM2.5-2.6B—
Muse Spark 1.153.3%
Source
Not directly comparable
OSWorld 2.0
LFM2.5-2.6B—
Muse Spark 1.114.2%
Source
Not directly comparable
JobBench
LFM2.5-2.6B—
Muse Spark 1.154.7%
Source
Not directly comparable
Cybench
LFM2.5-2.6B—
Muse Spark 1.192.9%
Source
Not directly comparable
ExploitGym
LFM2.5-2.6B—
Muse Spark 1.10.8%
Source
Not directly comparable
Terminal-Bench 2.1 (Vals)
LFM2.5-2.6B—
Muse Spark 1.169.3%
Source
Not directly comparable

Coding

LiveCodeBench v6
LFM2.5-2.6B59.4%
Source
Muse Spark 1.1—
Not directly comparable
Terminal-Bench 2.0
LFM2.5-2.6B—
Muse Spark 1.180.0%
Source
Not directly comparable
SWE-bench Pro
LFM2.5-2.6B—
Muse Spark 1.161.5%
Source
Not directly comparable
LiveCodeBench (Vals)
LFM2.5-2.6B—
Muse Spark 1.185.9%
Source
Not directly comparable
SWE-bench (Vals)
LFM2.5-2.6B—
Muse Spark 1.182.0%
Source
Not directly comparable

Reasoning

MRCR 1M
LFM2.5-2.6B—
Muse Spark 1.154.1%
Source
Not directly comparable

Knowledge

HLE
LFM2.5-2.6B—
Muse Spark 1.162.1%
Source
Not directly comparable
HLE w/o tools
LFM2.5-2.6B—
Muse Spark 1.152.2%
Source
Not directly comparable
HealthBench Professional
LFM2.5-2.6B—
Muse Spark 1.159.3%
Source
Not directly comparable
GPQA Diamond (Vals)
LFM2.5-2.6B—
Muse Spark 1.191.2%
Source
Not directly comparable
MMLU-Pro (Vals)
LFM2.5-2.6B—
Muse Spark 1.188.7%
Source
Not directly comparable

Math

AIME 2025
LFM2.5-2.6B51.9%
Source
Muse Spark 1.1—
Not directly comparable

Multimodal

CharXiv
LFM2.5-2.6B—
Muse Spark 1.188.4%
Source
Not directly comparable
BabyVision
LFM2.5-2.6B—
Muse Spark 1.176.3%
Source
Not directly comparable

Instruction following

IFBench
LFM2.5-2.6B59.2%
Source
Muse Spark 1.1—
Not directly comparable

Questions

Which is better, LFM2.5-2.6B or Muse Spark 1.1?

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, LFM2.5-2.6B or Muse Spark 1.1?

Muse Spark 1.1 scores higher for coding on the public lane, 59.5 to 39.2. LFM2.5-2.6B is 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, LFM2.5-2.6B or Muse Spark 1.1?

Muse Spark 1.1 scores higher for agentic tasks on the public lane, 60 to 37.2. LFM2.5-2.6B is scored on Estimated evidence for agentic tasks, so the reading is directional rather than like-for-like. BenchLM does not name a winner for agentic tasks from a directional reading; read the shared benchmark rows directly and test the models on the same task set.

Which costs less, LFM2.5-2.6B or Muse Spark 1.1?

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, LFM2.5-2.6B or Muse Spark 1.1?

Muse Spark 1.1 has the larger documented context window: 1M, compared with 128K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated September 18, 2026

Watch LFM2.5-2.6B vs Muse Spark 1.1

One weekly email when material rank, price, or benchmark evidence changes make this matchup worth revisiting.

Read a sample issue

Join 2,000+ readers.

LFM2.5-2.6B vs Muse Spark 1.1

Which one for your work

Long documents

Coding work

Agentic work

Chat turn cost

Cache-heavy agent loop cost

Repository review cost

Which one for a specific job

What is actually comparable

Category results, on a stated basis

Agentic

Coding

Knowledge

Reasoning

Math

Multilingual

Multimodal

Instruction following

Shape of the matched evidence

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

Benchmark evidence

Agentic

Coding

Reasoning

Knowledge

Math

Multimodal

Instruction following

Questions

Which is better, LFM2.5-2.6B or Muse Spark 1.1?

Which is better for coding, LFM2.5-2.6B or Muse Spark 1.1?

Which is better for agentic tasks, LFM2.5-2.6B or Muse Spark 1.1?

Which costs less, LFM2.5-2.6B or Muse Spark 1.1?

Which has the larger context window, LFM2.5-2.6B or Muse Spark 1.1?

Related comparisons

Watch LFM2.5-2.6B vs Muse Spark 1.1