Updated October 25, 2026. We do not rank this pair: at least one has no public score. Public scores include evidence status and uncertainty. This is a same-family comparison, so migration details appear when the source data supports them.
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. 10 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
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.
Code generation, repair, and software-engineering tasks
Laguna M.1
Laguna M.1 has the higher public coding point estimate, 25.9 to 22, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Laguna M.1 and Laguna XS.2 are not ranked on the public lane for agentic, so no winner is named for agentic.
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
1K fresh input + 500 output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
200K cached + 20K fresh input + 10K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
50K fresh input + 3K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
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.
Like-for-like · BenchAlign v5.8
Laguna M.1 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.
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
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.
Terminal-Bench 2.0Agentic
Normalized gap 10.1SWE MultilingualCoding
Normalized gap 5.4SWE-bench VerifiedCoding
Normalized gap 4.7SWE-bench ProCoding
Normalized gap 2.9LiveCodeBench (Vals)Coding
Normalized gap 0.3Each 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.
| Category | Laguna M.1 | Laguna XS.2 | Basis | Reading |
|---|---|---|---|---|
| Coding | 25.9Supported · #109/153 | 22.0Supported · #119/153 | Like-for-likeBenchAlign v5.8 lane · 6 vs 6 public rows | Laguna M.1 leads · intervals overlap |
| Agentic | Not ranked | Not ranked | Not comparableBenchAlign v5.8 lane · 2 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 | Not ranked | Not comparableBenchAlign v5.8 lane · 2 vs 2 public rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | 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.
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.
1K fresh input + 500 output tokens
Laguna M.1 has no comparable published API token rate. Laguna XS.2 has no comparable published API token rate.
50K fresh input + 3K output tokens
Laguna M.1 has no comparable published API token rate. Laguna XS.2 has no comparable published API token rate.
200K cached + 20K fresh input + 10K output tokens
Laguna M.1 has no comparable published API token rate. Laguna XS.2 has no comparable published API token rate.
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
Maximum documented context; output-token limits may be lower.
Laguna M.1
256K
Laguna XS.2
256K
Laguna M.1
Not sourced
Laguna XS.2
Not sourced
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Laguna M.1
No comparable hosted API rate
Laguna XS.2
No comparable hosted API rate
Laguna M.1
Not sourced
Laguna XS.2
Not sourced
Laguna M.1
Not sourced
Laguna XS.2
Not sourced
Laguna M.1
Not sourced
Laguna XS.2
Not sourced
Laguna M.1
Reasoning
Laguna XS.2
Reasoning
Laguna M.1
Proprietary
Laguna XS.2
Open Weight
Laguna M.1
Proprietary
Laguna XS.2
Open Weight
Laguna M.1
2026-04-28
Laguna XS.2
2026-04-28
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.
Laguna M.1 has the higher public coding point estimate, 25.9 to 22, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Laguna M.1 and Laguna XS.2 are not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Both models do not have comparable published API token rates, so this page does not name a universal price winner.
Both models list the same context window, 256K.
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Terminal-Bench 2.0
Shared sourceLaguna M.1 leads this result
Terminal-Bench 2.1 (Vals)
Laguna M.1 leads this result
SWE-bench Verified
Shared sourceLaguna M.1 leads this result
SWE Multilingual
Shared sourceLaguna M.1 leads this result
SWE-bench Pro
Shared sourceLaguna M.1 leads this result
Terminal-Bench 2.0
Shared sourceLaguna M.1 leads this result
LiveCodeBench (Vals)
Laguna M.1 leads this result
SWE-bench (Vals)
Laguna M.1 leads this result
One weekly email when material rank, price, or benchmark evidence changes make this matchup worth revisiting.
Read a sample issueJoin 6,100+ subscribers.
Last updated October 25, 2026
