Updated October 2, 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
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. 6 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
Gemini 3.5 Flash
Gemini 3.5 Flash has the higher public coding point estimate, 52.3 to 24, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Prompts that approach the documented context limit
Gemini 3.5 Flash
Gemini 3.5 Flash has the larger documented context window.
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Laguna XS.2 is not ranked on the public lane for agentic, so no winner is named for agentic.
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
Gemini 3.5 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.
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.
MMLU-Pro (Vals)Knowledge
Normalized gap 20.4LiveCodeBench (Vals)Coding
Normalized gap 19.8SWE-bench ProCoding
Normalized gap 8.8Each 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 | Gemini 3.5 Flash | Laguna XS.2 | Basis | Reading |
|---|---|---|---|---|
| Coding | 52.3Supported · #39/144 | 24.0Supported · #109/144 | Like-for-likeBenchAlign v5.8 lane · 7 vs 6 public rows | Gemini 3.5 Flash leads |
| Agentic | 50.7Supported · #42/119 | Not ranked | Not comparableBenchAlign v5.8 lane · 8 vs 2 public rows | Not comparable |
| Reasoning | 62.8#20/27 | Not ranked | Not comparableProvisional lane · 2 vs 0 weighted rows | Not comparable |
| Multimodal | 87.8#6/49 | Not ranked | Not comparableProvisional lane · 2 vs 0 weighted rows | Not comparable |
| Knowledge | 64.0Supported · #30/171 | Not ranked | Not comparableBenchAlign v5.8 lane · 4 vs 2 public rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | 84.1#43/125 | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 55.1Unranked · 2 rankable rows | Not ranked | Not comparableProvisional lane · 2 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 XS.2 has no comparable published API token rate.
50K fresh input + 3K output tokens
Laguna XS.2 has no comparable published API token rate.
200K cached + 20K fresh input + 10K output tokens
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.
Gemini 3.5 Flash
Laguna XS.2
256K
Gemini 3.5 Flash
gemini-3.5-flash
Google Gemini API pricingLaguna XS.2
Not sourced
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Gemini 3.5 Flash
$0.15 per 1M cached input tokens
Google Gemini API pricingLaguna XS.2
No comparable hosted API rate
Gemini 3.5 Flash
Not sourced
Laguna XS.2
Not sourced
Gemini 3.5 Flash
Not sourced
Laguna XS.2
Not sourced
Gemini 3.5 Flash
Not sourced
Laguna XS.2
Not sourced
Gemini 3.5 Flash
Reasoning
Laguna XS.2
Reasoning
Gemini 3.5 Flash
Proprietary
Laguna XS.2
Open Weight
Gemini 3.5 Flash
Proprietary
Laguna XS.2
Open Weight
Gemini 3.5 Flash
2026-05-19
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.
Gemini 3.5 Flash has the higher public coding point estimate, 52.3 to 24, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Laguna XS.2 is 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.
Gemini 3.5 Flash has the larger documented context window: 1M, compared with 256K.
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Terminal-Bench 2.1
Not directly comparable
MCP Atlas
Not directly comparable
Toolathlon
Not directly comparable
OSWorld-Verified
Not directly comparable
Finance Agent v2
Not directly comparable
Gert Labs
Not directly comparable
ResearchClawBench
Not directly comparable
Terminal-Bench 2.1 (Vals)
Gemini 3.5 Flash leads this result
Terminal-Bench 2.0
Not directly comparable
Terminal-Bench 2.1
Not directly comparable
SWE-bench Pro
Gemini 3.5 Flash leads this result
Vibe Code Bench
Not directly comparable
cursorBench31
Not directly comparable
CursorBench 3.2
Not directly comparable
LiveCodeBench (Vals)
Gemini 3.5 Flash leads this result
SWE-bench (Vals)
Gemini 3.5 Flash leads this result
SWE-bench Verified
Not directly comparable
SWE Multilingual
Not directly comparable
Terminal-Bench 2.0
Not directly comparable
GPQA-D
Not directly comparable
HLE
Not directly comparable
GPQA Diamond (Vals)
Gemini 3.5 Flash leads this result
MMLU-Pro (Vals)
Gemini 3.5 Flash leads this result
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Last updated October 2, 2026
