Long documents
Prompts that approach the documented context limit
Qwen3.5 Flash
Qwen3.5 Flash has the larger documented context window.
Confidence: documented
Laguna M.1 vs Qwen3.5 Flash
Updated October 25, 2026. We do not rank this pair: at least one has no public score. Public scores include evidence status and uncertainty.
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. 4 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 4
- Laguna M.1 only
- 6
- Qwen3.5 Flash only
- 1
- Like-for-like categories
- 0 / 8
Estimated: Qwen3.5 Flash. 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.
Show secondary and unsupported calls
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Qwen3.5 Flash is 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
Laguna M.1 and Qwen3.5 Flash are not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limitedChat 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-ratesCache-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: rate-fallbackRepository 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.
The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.
25.9Laguna M.127.0Qwen3.5 Flash
Directional only · BenchAlign v5.8
Qwen3.5 Flash 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.
6 Laguna M.1 only
4 Shared
1 Qwen3.5 Flash only
1 category rests 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.
MMLU-Pro (Vals)Knowledge
Normalized gap 15.3Laguna M.1:68.8%Qwen3.5 Flash:84.1%LiveCodeBench (Vals)Coding
Normalized gap 15.2Laguna M.1:68.1%Qwen3.5 Flash:83.3%
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- Laguna M.1
- 25.9
- Supported · #109/153
- Qwen3.5 Flash
- 27.0
- Estimated · #106/153
- Basis
- BenchAlign v5.8 lane · 6 vs 2 public rows
- Reading
- Directional only
Agentic
Not comparable- Laguna M.1
- Not ranked
- Qwen3.5 Flash
- Not ranked
- Basis
- BenchAlign v5.8 lane · 2 vs 0 public rows
- Reading
- Not comparable
Reasoning
Not comparable- Laguna M.1
- Not ranked
- Qwen3.5 Flash
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Laguna M.1
- Not ranked
- Qwen3.5 Flash
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- Laguna M.1
- Not ranked
- Qwen3.5 Flash
- 43.9
- Estimated · #101/187
- Basis
- BenchAlign v5.8 lane · 2 vs 2 public rows
- Reading
- Not comparable
Multilingual
Not comparable- Laguna M.1
- Not ranked
- Qwen3.5 Flash
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Laguna M.1
- Not ranked
- Qwen3.5 Flash
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- Laguna M.1
- Not ranked
- Qwen3.5 Flash
- 36.3
- Unranked · 1 rankable row
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Not comparable
| Category | Laguna M.1 | Qwen3.5 Flash | Basis | Reading |
|---|---|---|---|---|
| Coding | 25.9Supported · #109/153 | 27.0Estimated · #106/153 | Directional onlyBenchAlign v5.8 lane · 6 vs 2 public rows | Directional only |
| Agentic | Not ranked | Not ranked | Not comparableBenchAlign v5.8 lane · 2 vs 0 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 | 43.9Estimated · #101/187 | 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 | 36.3Unranked · 1 rankable row | Not comparableProvisional lane · 0 vs 1 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
- Laguna M.1
- API rate not published
- Fits in one request
- Qwen3.5 Flash
- $0.0003
- Fits in one request
Laguna M.1 has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- Laguna M.1
- API rate not published
- Fits in one request
- Qwen3.5 Flash
- $0.0062
- Fits in one request
Laguna M.1 has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- Laguna M.1
- API rate not published
- Fits in one request
- Cached-input rate unavailable
- Qwen3.5 Flash
- $0.026
- Fits in one request
- Cached input priced at the published list-input rate
Qwen3.5 Flash has no published cached-input rate, so cached tokens use its listed input rate. Laguna M.1 has no comparable published API token 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.
SpecificationLaguna M.1Qwen3.5 Flash
Context window
Maximum documented context; output-token limits may be lower.
Laguna M.1
256K
Qwen3.5 Flash
1M
API model ID
Laguna M.1
Not sourced
Qwen3.5 Flash
Not sourced
Cached-input rate
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
Qwen3.5 Flash
Not published
Documented inputs
Laguna M.1
Not sourced
Qwen3.5 Flash
Not sourced
Documented outputs
Laguna M.1
Not sourced
Qwen3.5 Flash
Not sourced
Provider availability
Laguna M.1
Not sourced
Qwen3.5 Flash
Not sourced
Reasoning profile
Laguna M.1
Reasoning
Qwen3.5 Flash
Reasoning
Weight access
Laguna M.1
Proprietary
Qwen3.5 Flash
Proprietary
License
Laguna M.1
Proprietary
Qwen3.5 Flash
Proprietary
Release date
Laguna M.1
2026-04-28
Qwen3.5 Flash
2026-03-04
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
- A complete comparable API-rate estimate is not available for both models.
- Context tradeoff
- Qwen3.5 Flash has the larger documented window (1M).
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, Laguna M.1 or Qwen3.5 Flash?
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 M.1 or Qwen3.5 Flash?
Qwen3.5 Flash scores higher for coding on the public lane, 27 to 25.9. Qwen3.5 Flash 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, Laguna M.1 or Qwen3.5 Flash?
Laguna M.1 and Qwen3.5 Flash are not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, Laguna M.1 or Qwen3.5 Flash?
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, Laguna M.1 or Qwen3.5 Flash?
Qwen3.5 Flash has the larger documented context window: 1M, compared with 256K.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence11 rows
Agentic
Coding
SWE-bench Verified
Laguna M.174.6%SourceQwen3.5 Flash—Not directly comparable
SWE Multilingual
Laguna M.163.1%SourceQwen3.5 Flash—Not directly comparable
SWE-bench Pro
Laguna M.149.2%SourceQwen3.5 Flash—Not directly comparable
Terminal-Bench 2.0
Laguna M.145.8%SourceQwen3.5 Flash—Not directly comparable
LiveCodeBench (Vals)
Laguna M.168.1%SourceQwen3.5 Flash83.3%SourceQwen3.5 Flash leads this result
SWE-bench (Vals)
Laguna M.157.6%SourceQwen3.5 Flash64.4%SourceQwen3.5 Flash leads this result
Knowledge
Math
FrontierMath v2 (Tiers 1-3)
Laguna M.1—Qwen3.5 Flash18.246%SourceNot directly comparable
11 public results · 4 shared
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Last updated October 25, 2026