Coding work
Code generation, repair, and software-engineering tasks
Gemini 4 Argon
Gemini 4 Argon leads on the public coding lane, 68.4 to 24.3, with Supported evidence for both models and non-overlapping 90% intervals.
Confidence: stronger
Gemini 4 Argon vs Laguna XS.2
Updated September 30, 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
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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.
- Shared results
- 0
- Gemini 4 Argon only
- 17
- Laguna XS.2 only
- 10
- Like-for-like categories
- 1 / 8
Estimated: Gemini 4 ArgonHow 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
Agentic work
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.
Confidence: limitedLong documents
Prompts that approach the documented context limit
Not enough matched evidence
A complete context comparison is not sourced.
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: listed-ratesRepository 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.
68.4Gemini 4 Argon24.3Laguna XS.2
Like-for-like · BenchAlign v5.7
Gemini 4 Argon leads the like-for-like coding row.
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.
17 Gemini 4 Argon only
10 Laguna XS.2 only
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.
Category results, on a stated basis
Each row shows the public-lane category score for both models: the BenchAlign v5.7 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
Like-for-like- Gemini 4 Argon
- 68.4
- Supported · #8/144
- Laguna XS.2
- 24.3
- Supported · #110/144
- Basis
- BenchAlign v5.7 lane · 4 vs 6 public rows
- Reading
- Gemini 4 Argon leads
Agentic
Not comparable- Gemini 4 Argon
- 63.7
- Estimated · #14/119
- Laguna XS.2
- Not ranked
- Basis
- BenchAlign v5.7 lane · 7 vs 2 public rows
- Reading
- Not comparable
Reasoning
Not comparable- Gemini 4 Argon
- 77.1
- Unranked · 3 rankable rows
- Laguna XS.2
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Gemini 4 Argon
- Not ranked
- Laguna XS.2
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- Gemini 4 Argon
- 72.9
- Supported · #11/170
- Laguna XS.2
- Not ranked
- Basis
- BenchAlign v5.7 lane · 1 vs 2 public rows
- Reading
- Not comparable
Multilingual
Not comparable- Gemini 4 Argon
- Not ranked
- Laguna XS.2
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Gemini 4 Argon
- Not ranked
- Laguna XS.2
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- Gemini 4 Argon
- Not ranked
- Laguna XS.2
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | Gemini 4 Argon | Laguna XS.2 | Basis | Reading |
|---|---|---|---|---|
| Coding | 68.4Supported · #8/144 | 24.3Supported · #110/144 | Like-for-likeBenchAlign v5.7 lane · 4 vs 6 public rows | Gemini 4 Argon leads |
| Agentic | 63.7Estimated · #14/119 | Not ranked | Not comparableBenchAlign v5.7 lane · 7 vs 2 public rows | Not comparable |
| Reasoning | 77.1Unranked · 3 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 | 72.9Supported · #11/170 | Not ranked | Not comparableBenchAlign v5.7 lane · 1 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 v5.7) 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
- Gemini 4 Argon
- $0.007
- Fit state unavailable
- Laguna XS.2
- Self-hosted; infrastructure cost varies
- Fits in one request
Laguna XS.2 has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- Gemini 4 Argon
- $0.13
- Fit state unavailable
- Laguna XS.2
- Self-hosted; infrastructure cost varies
- Fits in one request
Laguna XS.2 has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- Gemini 4 Argon
- $0.16
- Fit state unavailable
- Laguna XS.2
- Self-hosted; infrastructure cost varies
- Fits in one request
- Cached-input rate unavailable
Laguna XS.2 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.
SpecificationGemini 4 ArgonLaguna XS.2
Context window
Maximum documented context; output-token limits may be lower.
Gemini 4 Argon
Not sourced
Google Gemini 4 Argon announcementLaguna XS.2
256K
API model ID
Gemini 4 Argon
Not sourced
Laguna XS.2
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Gemini 4 Argon
$0.1 per 1M cached input tokens
Google Gemini 4 Argon announcementLaguna XS.2
No comparable hosted API rate
Documented inputs
Gemini 4 Argon
Not sourced
Laguna XS.2
Not sourced
Documented outputs
Gemini 4 Argon
Not sourced
Laguna XS.2
Not sourced
Provider availability
Gemini 4 Argon
Limited Access · Fairwind Program
Google Gemini 4 Argon announcementLaguna XS.2
Not sourced
Reasoning profile
Gemini 4 Argon
Reasoning
Laguna XS.2
Reasoning
Weight access
Gemini 4 Argon
Proprietary
Laguna XS.2
Open Weight
License
Gemini 4 Argon
Proprietary
Laguna XS.2
Open Weight
Release date
Gemini 4 Argon
2026-09-30
Laguna XS.2
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
- A complete documented context comparison is not available.
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, Gemini 4 Argon or Laguna XS.2?
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, Gemini 4 Argon or Laguna XS.2?
Gemini 4 Argon leads the public coding lane, 68.4 to 24.3, with Supported evidence for both models and non-overlapping 90% intervals.
Which is better for agentic tasks, Gemini 4 Argon or Laguna XS.2?
Laguna XS.2 is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, Gemini 4 Argon or Laguna XS.2?
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, Gemini 4 Argon or Laguna XS.2?
A complete documented context-window comparison is not available.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence27 rows
Agentic
AutomationBench
Gemini 4 Argon51.3%SourceLaguna XS.2—Not directly comparable
Finance Agent v2
Gemini 4 Argon65.4%SourceLaguna XS.2—Not directly comparable
Terminal-Bench 4.0
Gemini 4 Argon57.40%SourceLaguna XS.2—Not directly comparable
Agents' Last Exam
Gemini 4 Argon39.5%SourceLaguna XS.2—Not directly comparable
OSWorld 2.0
Gemini 4 Argon69.2%SourceLaguna XS.2—Not directly comparable
CWE-bench v1
Gemini 4 Argon68.0%SourceLaguna XS.2—Not directly comparable
Terminal-Bench-Science 0.1 (6x verifier timeout)
Gemini 4 Argon57.6%SourceLaguna XS.2—Not directly comparable
Terminal-Bench 2.0
Gemini 4 Argon—Laguna XS.235.7%SourceNot directly comparable
Terminal-Bench 2.1 (Vals)
Gemini 4 Argon—Laguna XS.225.8%SourceNot directly comparable
Coding
DeepSWE
Gemini 4 Argon77.9%SourceLaguna XS.2—Not directly comparable
FrontierSWE v2
Gemini 4 Argon55.1%SourceLaguna XS.2—Not directly comparable
Vibe Code Bench
Gemini 4 Argon91.90%SourceLaguna XS.2—Not directly comparable
PostTrainBench v1.1
Gemini 4 Argon45.3%SourceLaguna XS.2—Not directly comparable
SWE-bench Verified
Gemini 4 Argon—Laguna XS.269.9%SourceNot directly comparable
SWE Multilingual
Gemini 4 Argon—Laguna XS.257.7%SourceNot directly comparable
SWE-bench Pro
Gemini 4 Argon—Laguna XS.246.3%SourceNot directly comparable
Terminal-Bench 2.0
Gemini 4 Argon—Laguna XS.235.7%SourceNot directly comparable
LiveCodeBench (Vals)
Gemini 4 Argon—Laguna XS.267.8%SourceNot directly comparable
SWE-bench (Vals)
Gemini 4 Argon—Laguna XS.255.2%SourceNot directly comparable
Reasoning
Multimodal
Knowledge
Instruction following
Gray Swan IPI (15 attempts)
Gemini 4 Argon0.7%SourceLaguna XS.2—Not directly comparable
27 public results · 0 shared
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Last updated September 30, 2026