Updated October 28, 2026. We do not rank this pair: at least one has no public score. Public scores include evidence status and uncertainty.
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.
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 4 Carbon
Gemini 4 Carbon has the higher public coding point estimate, 82.2 to 26.8, 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
Gemini 4 Carbon and Laguna M.1 are not ranked on the public lane for agentic, so no winner is named for agentic.
Prompts that approach the documented context limit
Not enough matched evidence
A complete context comparison is not sourced.
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 4 Carbon 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.
A shared-evidence shape is not available.
BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.
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.
| Category | Gemini 4 Carbon | Laguna M.1 | Basis | Reading |
|---|---|---|---|---|
| Coding | 82.2Supported · #3/156 | 26.8Supported · #117/156 | Like-for-likeBenchAlign v5.8 lane · 1 vs 6 public rows | Gemini 4 Carbon leads |
| Agentic | Not ranked | Not ranked | Not comparableBenchAlign v5.8 lane · 4 vs 2 public rows | Not comparable |
| Reasoning | 79.5Unranked · 2 rankable rows | 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 | 88.2Supported · #2/178 | Not ranked | Not comparableBenchAlign v5.8 lane · 0 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.
50K fresh input + 3K output tokens
Laguna M.1 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.
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 4 Carbon
Not sourced
Google Gemini 4 Carbon launchLaguna M.1
256K
Gemini 4 Carbon
Not sourced
Laguna M.1
Not sourced
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Gemini 4 Carbon
$0.1 per 1M cached input tokens
Google Gemini 4 Carbon launch postLaguna M.1
No comparable hosted API rate
Gemini 4 Carbon
Not sourced
Laguna M.1
Not sourced
Gemini 4 Carbon
Not sourced
Laguna M.1
Not sourced
Gemini 4 Carbon
Limited Access · Fairwind Program
Google Gemini 4 Carbon launchLaguna M.1
Not sourced
Gemini 4 Carbon
Reasoning
Laguna M.1
Reasoning
Gemini 4 Carbon
Proprietary
Laguna M.1
Proprietary
Gemini 4 Carbon
Proprietary
Laguna M.1
Proprietary
Gemini 4 Carbon
2026-10-28
Laguna M.1
2026-04-28
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.
Gemini 4 Carbon has the higher public coding point estimate, 82.2 to 26.8, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Gemini 4 Carbon and Laguna M.1 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.
A complete documented context-window comparison is not available.
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
AutomationBench
Not directly comparable
OSWorld 2.1 Offline
Not directly comparable
CWE-bench v1
Not directly comparable
ExploitGym
Not directly comparable
Terminal-Bench 2.0
Not directly comparable
Terminal-Bench 2.1 (Vals)
Not directly comparable
FrontierSWE v2
Not directly comparable
SWE-bench Verified
Not directly comparable
SWE Multilingual
Not directly comparable
SWE-bench Pro
Not directly comparable
Terminal-Bench 2.0
Not directly comparable
LiveCodeBench (Vals)
Not directly comparable
SWE-bench (Vals)
Not directly comparable
GraphWalks BFS 256K–1M
Not directly comparable
Gray Swan IPI (15 attempts)
Not directly comparable
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Last updated October 28, 2026
