Updated October 17, 2026. Rank says Claude Sonnet 4.6 is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
Claude Sonnet 4.6 has the higher public score estimate, 55.89 versus 54.28, but the 90% score intervals overlap. Treat that as a lead, not a settled winner. 9 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
Claude Sonnet 4.6
Claude Sonnet 4.6 has the higher public coding point estimate, 44.7 to 32.1, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Tool use, computer use, and multi-step task completion
Claude Sonnet 4.6
Claude Sonnet 4.6 has the higher public agentic point estimate, 46.3 to 32.4, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Prompts that approach the documented context limit
Gemini 3 Flash
Gemini 3 Flash has the larger documented context window.
1K fresh input + 500 output tokens
Gemini 3 Flash
Gemini 3 Flash has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
50K fresh input + 3K output tokens
Gemini 3 Flash
Gemini 3 Flash 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
Not enough matched evidence
The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. Claude Sonnet 4.6 does not fit this workload in one request. Claude Sonnet 4.6 has no published cached-input rate, so cached tokens use its listed input rate.
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
Claude Sonnet 4.6 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.
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.
LiveCodeBench (Vals)Coding
Normalized gap 3.5MMLU-Pro (Vals)Knowledge
Normalized gap 1.3Each 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 | Claude Sonnet 4.6 | Gemini 3 Flash | Basis | Reading |
|---|---|---|---|---|
| Agentic | 46.3Supported · #55/128 | 32.4Supported · #88/128 | Like-for-likeBenchAlign v5.8 lane · 10 vs 4 public rows | Claude Sonnet 4.6 leads · intervals overlap |
| Coding | 44.7Supported · #54/150 | 32.1Supported · #88/150 | Like-for-likeBenchAlign v5.8 lane · 9 vs 3 public rows | Claude Sonnet 4.6 leads · intervals overlap |
| Knowledge | 55.4Supported · #58/184 | 55.7Supported · #55/184 | Like-for-likeBenchAlign v5.8 lane · 6 vs 2 public rows | Practical tie |
| Instruction following | 48.0#99/148 | 66.1#76/148 | Directional onlyProvisional lane · 0 vs 0 weighted rows | Directional only |
| Reasoning | 62.8Unranked · 4 rankable rows | 63.8Unranked · 2 rankable rows | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Multimodal | 58.7#45/54 | 80.3Unranked · 1 rankable row | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | Not ranked | 57.4Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 2 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
Gemini 3 Flash has the lower modeled cost
Costs use the listed standard API rates.
50K fresh input + 3K output tokens
Gemini 3 Flash has the lower modeled cost
Costs use the listed standard API rates.
200K cached + 20K fresh input + 10K output tokens
Claude Sonnet 4.6 does not fit this workload in one request. Claude Sonnet 4.6 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 Sonnet 4.6
200K
Gemini 3 Flash
Claude Sonnet 4.6
Not sourced
Gemini 3 Flash
gemini-3-flash-preview
Google Gemini API pricingA missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Claude Sonnet 4.6
Not published
Gemini 3 Flash
$0.05 per 1M cached input tokens
Google Gemini API pricingClaude Sonnet 4.6
Not sourced
Gemini 3 Flash
Not sourced
Claude Sonnet 4.6
Not sourced
Gemini 3 Flash
Not sourced
Claude Sonnet 4.6
Not sourced
Gemini 3 Flash
Not sourced
Claude Sonnet 4.6
Non-Reasoning
Gemini 3 Flash
Non-Reasoning
Claude Sonnet 4.6
Proprietary
Gemini 3 Flash
Proprietary
Claude Sonnet 4.6
Proprietary
Gemini 3 Flash
Proprietary
Claude Sonnet 4.6
2026-02-01
Gemini 3 Flash
2025-12-01
Claude Sonnet 4.6 has the higher public score estimate, 55.89 versus 54.28, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
Claude Sonnet 4.6 has the higher public coding point estimate, 44.7 to 32.1, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Claude Sonnet 4.6 has the higher public agentic tasks point estimate, 46.3 to 32.4, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
For the stated presets, chat costs $0.0105 on Claude Sonnet 4.6 and $0.002 on Gemini 3 Flash; repository review costs $0.195 and $0.034; the cache-heavy agent loop costs $0.81 and $0.05. Claude Sonnet 4.6 does not fit this workload in one request. Claude Sonnet 4.6 has no published cached-input rate, so cached tokens use its listed input rate.
Gemini 3 Flash has the larger documented context window: 1M, compared with 200K.
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Terminal-Bench 2.0
Not directly comparable
OSWorld-Verified
Not directly comparable
Claw-Eval
Shared sourceClaude Sonnet 4.6 leads this result
CyberGym
Not directly comparable
Gert Labs
Shared sourceClaude Sonnet 4.6 leads this result
OSWorld 2.0
Not directly comparable
JobBench
Shared sourceClaude Sonnet 4.6 leads this result
Terminal-Bench 2.1 (Vals)
Claude Sonnet 4.6 leads this result
ApprenticeBench
Not directly comparable
MazeBench
Not directly comparable
SWE-bench Verified
Not directly comparable
SWE-Rebench
Not directly comparable
React Native Evals
Not directly comparable
Terminal-Bench 2.0
Not directly comparable
Vibe Code Bench
Shared sourceClaude Sonnet 4.6 leads this result
cursorBench31
Not directly comparable
FrontierCode 1.1 Main
Not directly comparable
LiveCodeBench (Vals)
Gemini 3 Flash leads this result
SWE-bench (Vals)
Claude Sonnet 4.6 leads this result
CharXiv
Not directly comparable
GPQA
Not directly comparable
SuperGPQA
Not directly comparable
MMLU-Pro
Not directly comparable
HLE
Not directly comparable
GPQA Diamond (Vals)
Gemini 3 Flash leads this result
MMLU-Pro (Vals)
Gemini 3 Flash leads this result
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Last updated October 17, 2026
