Coding work
Code generation, repair, and software-engineering tasks
Gemini 4 Argon
Gemini 4 Argon leads on the public coding lane, 68.4 to 63, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limited
Gemini 4 Argon vs GPT-6 Sol
Updated September 30, 2026. Rank says GPT-6 Sol is ahead. Price, access, and your workload can each overturn that. 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
Share or export
Decision reading
GPT-6 Sol has the higher public score estimate, 78.31 versus 64.59, but the 90% score intervals overlap. Treat that as a lead, not a settled winner. 6 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 6
- Gemini 4 Argon only
- 11
- GPT-6 Sol only
- 6
- Like-for-like categories
- 2 / 8
Estimated: Gemini 4 Argon and GPT-6 SolHow 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.
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Gemini 4 Argon
Gemini 4 Argon has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Show secondary and unsupported calls
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Gemini 4 Argon is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
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
No clear pick
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-ratesRepository review cost
50K fresh input + 3K output tokens
No clear pick
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 Argon63.0GPT-6 Sol
Like-for-like · BenchAlign v5.7
Gemini 4 Argon leads the like-for-like coding row, although the 90% intervals overlap.
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.
11 Gemini 4 Argon only
6 Shared
6 GPT-6 Sol 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.
AutomationBenchAgentic
Normalized gap 18.1Gemini 4 Argon:51.3%GPT-6 Sol:33.2%OSWorld 2.0Agentic
Normalized gap 8.7Gemini 4 Argon:69.2%GPT-6 Sol:60.5%
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 v5.7 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
- GPT-6 Sol
- 63.0
- Supported · #13/144
- Basis
- BenchAlign v5.7 lane · 4 vs 1 public rows
- Reading
- Gemini 4 Argon leads · intervals overlap
Knowledge
Like-for-like- Gemini 4 Argon
- 72.9
- Supported · #11/170
- GPT-6 Sol
- 78.8
- Supported · #7/170
- Basis
- BenchAlign v5.7 lane · 1 vs 5 public rows
- Reading
- GPT-6 Sol leads · intervals overlap
Agentic
Directional only- Gemini 4 Argon
- 63.7
- Estimated · #14/119
- GPT-6 Sol
- 60.0
- Supported · #19/119
- Basis
- BenchAlign v5.7 lane · 7 vs 5 public rows
- Reading
- Directional only
Reasoning
Not comparable- Gemini 4 Argon
- 77.1
- Unranked · 3 rankable rows
- GPT-6 Sol
- 79.6
- #4/27
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Gemini 4 Argon
- Not ranked
- GPT-6 Sol
- 83.8
- Unranked · 1 rankable row
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- Gemini 4 Argon
- Not ranked
- GPT-6 Sol
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Gemini 4 Argon
- Not ranked
- GPT-6 Sol
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- Gemini 4 Argon
- Not ranked
- GPT-6 Sol
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | Gemini 4 Argon | GPT-6 Sol | Basis | Reading |
|---|---|---|---|---|
| Coding | 68.4Supported · #8/144 | 63.0Supported · #13/144 | Like-for-likeBenchAlign v5.7 lane · 4 vs 1 public rows | Gemini 4 Argon leads · intervals overlap |
| Knowledge | 72.9Supported · #11/170 | 78.8Supported · #7/170 | Like-for-likeBenchAlign v5.7 lane · 1 vs 5 public rows | GPT-6 Sol leads · intervals overlap |
| Agentic | 63.7Estimated · #14/119 | 60.0Supported · #19/119 | Directional onlyBenchAlign v5.7 lane · 7 vs 5 public rows | Directional only |
| Reasoning | 77.1Unranked · 3 rankable rows | 79.6#4/27 | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | Not ranked | 83.8Unranked · 1 rankable row | Not comparableProvisional lane · 0 vs 0 weighted 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
- GPT-6 Sol
- $0.007
- Fits in one request
Modeled costs are equal
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- Gemini 4 Argon
- $0.13
- Fit state unavailable
- GPT-6 Sol
- $0.13
- Fits in one request
Modeled costs are equal
Costs use the listed standard API rates.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- Gemini 4 Argon
- $0.16
- Fit state unavailable
- GPT-6 Sol
- $0.18
- Fits in one request
Gemini 4 Argon has the lower modeled cost
Costs use the listed standard API rates.
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 ArgonGPT-6 Sol
Context window
Maximum documented context; output-token limits may be lower.
Gemini 4 Argon
Not sourced
Google Gemini 4 Argon announcementGPT-6 Sol
API model ID
Gemini 4 Argon
Not sourced
GPT-6 Sol
gpt-6-sol
OpenAI GPT-6 Sol model documentationCached-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 announcementGPT-6 Sol
$0.2 per 1M cached input tokens
OpenAI GPT-6 Sol model documentationDocumented inputs
Gemini 4 Argon
Not sourced
GPT-6 Sol
text, image
OpenAI GPT-6 Sol model documentationDocumented outputs
Gemini 4 Argon
Not sourced
GPT-6 Sol
Provider availability
Gemini 4 Argon
Limited Access · Fairwind Program
Google Gemini 4 Argon announcementGPT-6 Sol
Generally Available · OpenAI Responses API, OpenAI Chat Completions API, ChatGPT Work, Codex
OpenAI GPT-6 Sol and Luna launchReasoning profile
Gemini 4 Argon
Reasoning
GPT-6 Sol
Reasoning
Weight access
Gemini 4 Argon
Proprietary
GPT-6 Sol
Proprietary
License
Gemini 4 Argon
Proprietary
GPT-6 Sol
Proprietary
Release date
Gemini 4 Argon
2026-09-30
GPT-6 Sol
2026-09-22
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
- GPT-6 Sol has the higher public score estimate, 78.31 versus 64.59, but the 90% score intervals overlap.
- Workload cost
- Repository review: $0.13 vs $0.13. Cache-heavy agent loop: $0.16 vs $0.18.
- 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 GPT-6 Sol?
GPT-6 Sol has the higher public score estimate, 78.31 versus 64.59, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
Which is better for coding, Gemini 4 Argon or GPT-6 Sol?
Gemini 4 Argon leads the public coding lane, 68.4 to 63, with Supported evidence for both models, although the 90% intervals overlap.
Which is better for agentic tasks, Gemini 4 Argon or GPT-6 Sol?
Gemini 4 Argon scores higher for agentic tasks on the public lane, 63.7 to 60. Gemini 4 Argon is scored on Estimated evidence for agentic tasks, so the reading is directional rather than like-for-like. BenchLM does not name a winner for agentic tasks from a directional reading; read the shared benchmark rows directly and test the models on the same task set.
Which costs less, Gemini 4 Argon or GPT-6 Sol?
For the stated presets, chat costs $0.007 on Gemini 4 Argon and $0.007 on GPT-6 Sol; repository review costs $0.13 and $0.13; the cache-heavy agent loop costs $0.16 and $0.18. Costs use the listed standard API rates.
Which has the larger context window, Gemini 4 Argon or GPT-6 Sol?
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 evidence23 rows
Agentic
AutomationBench
Gemini 4 Argon51.3%SourceGPT-6 Sol33.2%SourceGemini 4 Argon leads this result
Finance Agent v2
Gemini 4 Argon65.4%SourceGPT-6 Sol—Not directly comparable
Terminal-Bench 4.0
Gemini 4 Argon57.40%SourceGPT-6 Sol—Not directly comparable
Agents' Last Exam
Gemini 4 Argon39.5%SourceGPT-6 Sol56.4%SourceGPT-6 Sol leads this result
OSWorld 2.0
Gemini 4 Argon69.2%SourceGPT-6 Sol60.5%SourceGemini 4 Argon leads this result
CWE-bench v1
Shared sourceGemini 4 Argon68.0%GPT-6 Sol52.0%Gemini 4 Argon leads this result
Terminal-Bench-Science 0.1 (6x verifier timeout)
Gemini 4 Argon57.6%SourceGPT-6 Sol—Not directly comparable
ExploitGym
Gemini 4 Argon—GPT-6 Sol22.1%SourceNot directly comparable
Coding
DeepSWE
Gemini 4 Argon77.9%SourceGPT-6 Sol68.8%SourceGemini 4 Argon leads this result
FrontierSWE v2
Gemini 4 Argon55.1%SourceGPT-6 Sol—Not directly comparable
Vibe Code Bench
Gemini 4 Argon91.90%SourceGPT-6 Sol—Not directly comparable
PostTrainBench v1.1
Gemini 4 Argon45.3%SourceGPT-6 Sol—Not directly comparable
Reasoning
Multimodal
Knowledge
LABBench2
Gemini 4 Argon88.8%SourceGPT-6 Sol—Not directly comparable
HealthBench (raw)
Gemini 4 Argon—GPT-6 Sol47.1%SourceNot directly comparable
HealthBench (length-adjusted)
Gemini 4 Argon—GPT-6 Sol53.2%SourceNot directly comparable
HealthBench Professional
Gemini 4 Argon—GPT-6 Sol60.8%SourceNot directly comparable
HealthBench Professional (raw)
Gemini 4 Argon—GPT-6 Sol59.5%SourceNot directly comparable
HealthBench Hard
Gemini 4 Argon—GPT-6 Sol30.1%SourceNot directly comparable
Instruction following
Gray Swan IPI (15 attempts)
Shared sourceGemini 4 Argon0.7%GPT-6 Sol10.1%Gemini 4 Argon leads this result
23 public results · 6 shared
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Last updated September 30, 2026