Coding work
Code generation, repair, and software-engineering tasks
Gemini 4 Argon
Gemini 4 Argon leads on the public coding lane, 68.4 to 62.5, with Supported evidence for both models, although the 90% intervals overlap.
Updated September 30, 2026. Rank says Claude Opus 4.8 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
Claude Opus 4.8 has the higher public score estimate, 70 versus 64.59, but the 90% score intervals overlap. Treat that as a lead, not a settled winner. 3 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 Argon
Gemini 4 Argon leads on the public coding lane, 68.4 to 62.5, with Supported evidence for both models, although the 90% intervals overlap.
1K fresh input + 500 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.
200K cached + 20K fresh input + 10K output tokens
Gemini 4 Argon
Gemini 4 Argon has the lower estimated token cost for this stated workload. Claude Opus 4.8 has no published cached-input rate, so cached tokens use its listed input rate.
50K fresh input + 3K 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.
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.
Prompts that approach the documented context limit
Not enough matched evidence
A complete context comparison is not sourced.
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 v5.7
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.
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
1 category rests on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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.
OSWorld 2.0Agentic
Normalized gap 48.6Each 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.
| Category | Claude Opus 4.8 | Gemini 4 Argon | Basis | Reading |
|---|---|---|---|---|
| Coding | 62.5Supported · #15/144 | 68.4Supported · #8/144 | Like-for-likeBenchAlign v5.7 lane · 11 vs 4 public rows | Gemini 4 Argon leads · intervals overlap |
| Knowledge | 70.0Supported · #14/170 | 72.9Supported · #11/170 | Like-for-likeBenchAlign v5.7 lane · 6 vs 1 public rows | Gemini 4 Argon leads · intervals overlap |
| Agentic | 61.7Supported · #16/119 | 63.7Estimated · #14/119 | Directional onlyBenchAlign v5.7 lane · 12 vs 7 public rows | Directional only |
| Reasoning | 59.1#22/27 | 77.1Unranked · 3 rankable rows | Not comparableProvisional lane · 2 vs 0 weighted rows | Not comparable |
| Multimodal | 87.7#7/49 | Not ranked | Not comparableProvisional lane · 2 vs 0 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | 74.0#60/124 | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 65.2#2/7 | Not ranked | Not comparableProvisional lane · 3 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.
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
Gemini 4 Argon has the lower modeled cost
Costs use the listed standard API rates.
50K fresh input + 3K output tokens
Gemini 4 Argon has the lower modeled cost
Costs use the listed standard API rates.
200K cached + 20K fresh input + 10K output tokens
Gemini 4 Argon has the lower modeled cost
Claude Opus 4.8 has no published cached-input rate, so cached tokens use its listed input 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.
Claude Opus 4.8
Gemini 4 Argon
Not sourced
Google Gemini 4 Argon announcementClaude Opus 4.8
claude-opus-4-8
Anthropic model overviewGemini 4 Argon
Not sourced
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Claude Opus 4.8
Not published
Gemini 4 Argon
$0.1 per 1M cached input tokens
Google Gemini 4 Argon announcementClaude Opus 4.8
text, image
Anthropic model overviewGemini 4 Argon
Not sourced
Claude Opus 4.8
Gemini 4 Argon
Not sourced
Claude Opus 4.8
Generally Available · Claude API
Anthropic model overviewGemini 4 Argon
Limited Access · Fairwind Program
Google Gemini 4 Argon announcementClaude Opus 4.8
Reasoning
Gemini 4 Argon
Reasoning
Claude Opus 4.8
Proprietary
Gemini 4 Argon
Proprietary
Claude Opus 4.8
Proprietary
Gemini 4 Argon
Proprietary
Claude Opus 4.8
2026-05-28
Gemini 4 Argon
2026-09-30
Claude Opus 4.8 has the higher public score estimate, 70 versus 64.59, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
Gemini 4 Argon leads the public coding lane, 68.4 to 62.5, with Supported evidence for both models, although the 90% intervals overlap.
Gemini 4 Argon scores higher for agentic tasks on the public lane, 63.7 to 61.7. 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.
For the stated presets, chat costs $0.0175 on Claude Opus 4.8 and $0.007 on Gemini 4 Argon; repository review costs $0.325 and $0.13; the cache-heavy agent loop costs $1.35 and $0.16. Claude Opus 4.8 has no published cached-input rate, so cached tokens use its listed input rate.
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.
Terminal-Bench 3.0
Not directly comparable
Terminal-Bench 2.1
Not directly comparable
BrowseComp
Not directly comparable
DeepSearchQA
Not directly comparable
OSWorld-Verified
Not directly comparable
Finance Agent v2
Gemini 4 Argon leads this result
MCP Atlas
Not directly comparable
Toolathlon
Not directly comparable
Gert Labs
Not directly comparable
ResearchClawBench
Not directly comparable
OSWorld 2.0
Gemini 4 Argon leads this result
Terminal-Bench 2.1 (Vals)
Not directly comparable
AutomationBench
Not directly comparable
Terminal-Bench 4.0
Not directly comparable
Agents' Last Exam
Not directly comparable
CWE-bench v1
Not directly comparable
Terminal-Bench-Science 0.1 (6x verifier timeout)
Not directly comparable
SWE-bench Verified
Not directly comparable
SWE-bench Pro
Not directly comparable
SWE Multilingual
Not directly comparable
SWE Multimodal
Not directly comparable
Terminal-Bench 2.1
Not directly comparable
cursorBench31
Not directly comparable
cursorBench32
Not directly comparable
FrontierCode 1.1 Main
Not directly comparable
LiveCodeBench (Vals)
Not directly comparable
SWE-bench (Vals)
Not directly comparable
PostTrainBench v1.1
Gemini 4 Argon leads this result
DeepSWE
Not directly comparable
FrontierSWE v2
Not directly comparable
Vibe Code Bench
Not directly comparable
ARC-AGI-2
Not directly comparable
ARC-AGI-3
Not directly comparable
Graphwalks BFS 128K
Not directly comparable
GraphWalks BFS 256K–1M
Not directly comparable
OfficeQA Pro
Not directly comparable
ScreenSpot Pro
Not directly comparable
CharXiv
Not directly comparable
CharXiv w/o tools
Not directly comparable
Chartography (no tools)
Not directly comparable
LVBench
Not directly comparable
GPQA
Not directly comparable
GPQA-D
Not directly comparable
HLE
Not directly comparable
HLE w/o tools
Not directly comparable
GPQA Diamond (Vals)
Not directly comparable
MMLU-Pro (Vals)
Not directly comparable
LABBench2
Not directly comparable
INCLUDE
Not directly comparable
Gray Swan IPI (15 attempts)
Not directly comparable
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Last updated September 30, 2026