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BenchLM
NVIDIA logo
Model A
Cosmos3-Edge

NVIDIA

—

Evidence status unavailable

90% interval unavailable

Cosmos3-Edge vs Laguna M.1

Updated September 4, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Poolside logo
Model B
Laguna M.1

Poolside

4.46/100

Estimated · Public rank #240

90% interval 0.0–14.3

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.

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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.

No workload recommendation clears the current evidence threshold.

Use the matched evidence, workload costs, and sourced specifications below instead of treating a point score as a universal answer.

Show secondary and unsupported calls

  • Coding work

    Code generation, repair, and software-engineering tasks

    Not enough matched evidence

    Cosmos3-Edge 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

    Cosmos3-Edge is not ranked on the public lane for agentic, so no winner is named for agentic.

    Confidence: limited

  • Long documents

    Prompts that approach the documented context limit

    No clear pick

    The documented context windows are equal.

    Confidence: documented

  • 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

    A complete comparable API-rate estimate is not available for both models.

    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

What is actually comparable

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

Shared results
0
Cosmos3-Edge only
0
Laguna M.1 only
10
Like-for-like categories
0 / 8

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

Not comparable
Cosmos3-Edge
Not ranked
Laguna M.1
25.5
Estimated · #146/151
Basis
BenchAlign lane · 0 vs 2 public rows
Reading
Not comparable

Coding

Not comparable
Cosmos3-Edge
Not ranked
Laguna M.1
30.2
Supported · #169/183
Basis
BenchAlign lane · 0 vs 6 public rows
Reading
Not comparable

Reasoning

Not comparable
Cosmos3-Edge
Not ranked
Laguna M.1
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Knowledge

Not comparable
Cosmos3-Edge
Not ranked
Laguna M.1
19.9
Estimated · #179/181
Basis
BenchAlign lane · 0 vs 2 public rows
Reading
Not comparable

Math

Not comparable
Cosmos3-Edge
Not ranked
Laguna M.1
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multilingual

Not comparable
Cosmos3-Edge
Not ranked
Laguna M.1
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
Cosmos3-Edge
Not ranked
Laguna M.1
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
Cosmos3-Edge
Not ranked
Laguna M.1
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) 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

Cosmos3-Edge
Self-hosted; infrastructure cost varies
Fits in one request
Laguna M.1
API rate not published
Fits in one request

Cosmos3-Edge has no comparable published API token rate. Laguna M.1 has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Cosmos3-Edge
Self-hosted; infrastructure cost varies
Fits in one request
Laguna M.1
API rate not published
Fits in one request

Cosmos3-Edge has no comparable published API token rate. Laguna M.1 has no comparable published API token rate.

Cache-heavy agent loop

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

Cosmos3-Edge
Self-hosted; infrastructure cost varies
Fits in one request
Cached-input rate unavailable
Laguna M.1
API rate not published
Fits in one request
Cached-input rate unavailable

Cosmos3-Edge has no comparable published API token rate. Laguna M.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.

Context window

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

Cosmos3-Edge

256K

Laguna M.1

256K

API model ID

Cosmos3-Edge

Not sourced

Laguna M.1

Not sourced

Cached-input rate

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

Cosmos3-Edge

No comparable hosted API rate

Laguna M.1

No comparable hosted API rate

Documented inputs

Cosmos3-Edge

Not sourced

Laguna M.1

Not sourced

Documented outputs

Cosmos3-Edge

Not sourced

Laguna M.1

Not sourced

Provider availability

Cosmos3-Edge

Not sourced

Laguna M.1

Not sourced

Reasoning profile

Cosmos3-Edge

Reasoning

Laguna M.1

Reasoning

Weight access

Cosmos3-Edge

Open Weight

Laguna M.1

Proprietary

License

Cosmos3-Edge

Open Weight

Laguna M.1

Proprietary

Release date

Cosmos3-Edge

2026-07-20

Laguna M.1

2026-04-28

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
Both models list 256K.

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 evidence10 rows

Agentic

  • Terminal-Bench 2.0

    Cosmos3-Edge—
    Laguna M.145.8%
    Source

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    Cosmos3-Edge—
    Laguna M.134.1%
    Source

    Not directly comparable

Coding

  • SWE-bench Verified

    Cosmos3-Edge—
    Laguna M.174.6%
    Source

    Not directly comparable

  • SWE Multilingual

    Cosmos3-Edge—
    Laguna M.163.1%
    Source

    Not directly comparable

  • SWE-bench Pro

    Cosmos3-Edge—
    Laguna M.149.2%
    Source

    Not directly comparable

  • Terminal-Bench 2.0

    Cosmos3-Edge—
    Laguna M.145.8%
    Source

    Not directly comparable

  • LiveCodeBench (Vals)

    Cosmos3-Edge—
    Laguna M.168.1%
    Source

    Not directly comparable

  • SWE-bench (Vals)

    Cosmos3-Edge—
    Laguna M.157.6%
    Source

    Not directly comparable

Knowledge

  • GPQA Diamond (Vals)

    Cosmos3-Edge—
    Laguna M.127.0%
    Source

    Not directly comparable

  • MMLU-Pro (Vals)

    Cosmos3-Edge—
    Laguna M.168.8%
    Source

    Not directly comparable

Frequently asked questions

Which is better, Cosmos3-Edge or Laguna M.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, Cosmos3-Edge or Laguna M.1?

Cosmos3-Edge is not ranked on the public lane for coding, so no winner is named for coding.

Which is better for agentic tasks, Cosmos3-Edge or Laguna M.1?

Cosmos3-Edge is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, Cosmos3-Edge or Laguna M.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, Cosmos3-Edge or Laguna M.1?

Both models list the same context window, 256K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated September 4, 2026

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