Agentic
Like-for-like- Claude Opus 4.6
- 50.1
- Supported · #56/152
- Grok 4.20
- 26.7
- Supported · #145/152
- Basis
- BenchAlign lane · 10 vs 4 public rows
- Reading
- Claude Opus 4.6 leads
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Follow model changesUpdated September 10, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
Claude Opus 4.6 has the higher public score estimate, 69 versus 67.13, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
14 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
Share or export
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 Opus 4.6
Claude Opus 4.6 leads on the public coding lane, 56.6 to 28.2, with Supported evidence for both models and non-overlapping 90% intervals.
Confidence: stronger
Tool use, computer use, and multi-step task completion
Claude Opus 4.6
Claude Opus 4.6 leads on the public agentic lane, 50.1 to 26.7, with Supported evidence for both models and non-overlapping 90% intervals.
Confidence: stronger
Prompts that approach the documented context limit
Grok 4.20
Grok 4.20 has the larger documented context window.
Confidence: documented
1K fresh input + 500 output tokens
Grok 4.20
Grok 4.20 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
200K cached + 20K fresh input + 10K output tokens
Grok 4.20
Grok 4.20 has the lower estimated token cost for this stated workload. Claude Opus 4.6 has no published cached-input rate, so cached tokens use its listed input rate. Grok 4.20 has no published cached-input rate, so cached tokens use its listed input rate.
Confidence: rate-fallback
50K fresh input + 3K output tokens
Grok 4.20
Grok 4.20 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
2 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
Each row shows the public-lane category score for both models: the BenchAlign 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 4.6 | Grok 4.20 | Basis | Reading |
|---|---|---|---|---|
| Agentic | 50.1Supported · #56/152 | 26.7Supported · #145/152 | Like-for-likeBenchAlign lane · 10 vs 4 public rows | Claude Opus 4.6 leads |
| Coding | 56.6Supported · #37/151 | 28.2Supported · #141/151 | Like-for-likeBenchAlign lane · 8 vs 6 public rows | Claude Opus 4.6 leads |
| Knowledge | 61.0Estimated · #34/183 | 49.4Supported · #85/183 | Directional onlyBenchAlign lane · 9 vs 6 public rows | Directional only |
| Multimodal | 59.5#28/48 | 34.6#43/48 | Directional onlyProvisional lane · 1 vs 2 weighted rows | Directional only |
| Reasoning | 67.1Unranked · 2 rankable rows | 34.2Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 2 weighted rows | Not comparable |
| Math | 58.6Unranked · 3 rankable rows | 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 | 52.6#78/123 | 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) differ from the provisional-lane categories. Unranked scores sit on the lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.
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.
Terminal-Bench 2.0
Agentic
HLE w/o tools
Knowledge
SWE-bench Verified
Coding
MMMU-Pro
Multimodal
SWE-bench Pro
Coding
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
Grok 4.20 has the lower modeled cost
Costs use the listed standard API rates.
50K fresh input + 3K output tokens
Grok 4.20 has the lower modeled cost
Costs use the listed standard API rates.
200K cached + 20K fresh input + 10K output tokens
Grok 4.20 has the lower modeled cost
Claude Opus 4.6 has no published cached-input rate, so cached tokens use its listed input rate. Grok 4.20 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 4.6
1M
Grok 4.20
2M
Claude Opus 4.6
Not sourced
Grok 4.20
Not sourced
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Claude Opus 4.6
Not published
Grok 4.20
Not published
Claude Opus 4.6
Not sourced
Grok 4.20
Not sourced
Claude Opus 4.6
Not sourced
Grok 4.20
Not sourced
Claude Opus 4.6
Not sourced
Grok 4.20
Not sourced
Claude Opus 4.6
Non-Reasoning
Grok 4.20
Reasoning
Claude Opus 4.6
Proprietary
Grok 4.20
Proprietary
Claude Opus 4.6
Proprietary
Grok 4.20
Proprietary
Claude Opus 4.6
2026-02-01
Grok 4.20
2026-03-10
Run the same representative tasks against both endpoints before changing production traffic.
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Terminal-Bench 2.0
Claude Opus 4.6 leads this result
BrowseComp
Not directly comparable
OSWorld-Verified
Not directly comparable
Claw-Eval
Not directly comparable
DeepSearchQA
Shared sourceClaude Opus 4.6 leads this result
CyberGym
Not directly comparable
Gert Labs
Shared sourceClaude Opus 4.6 leads this result
ResearchClawBench
Not directly comparable
JobBench
Not directly comparable
ApprenticeBench
Not directly comparable
Terminal-Bench 2.1 (Vals)
Not directly comparable
SWE-bench Verified
Claude Opus 4.6 leads this result
SWE-bench Verified*
Not directly comparable
LiveCodeBench Pro
Shared sourceGrok 4.20 leads this result
SWE-bench Pro
Shared sourceClaude Opus 4.6 leads this result
SWE-Rebench
Not directly comparable
React Native Evals
Not directly comparable
Vibe Code Bench
Shared sourceClaude Opus 4.6 leads this result
FrontierCode 1.1 Main
Not directly comparable
LiveCodeBench (Vals)
Not directly comparable
SWE-bench (Vals)
Not directly comparable
GPQA
Not directly comparable
GPQA-D
Claude Opus 4.6 leads this result
SuperGPQA
Not directly comparable
MMLU-Pro
Not directly comparable
MMLU-Pro (Arcee)
Not directly comparable
HLE
Not directly comparable
HLE w/o tools
Shared sourceClaude Opus 4.6 leads this result
HealthBench Hard
Shared sourceGrok 4.20 leads this result
MedXpertQA (Text)
Shared sourceClaude Opus 4.6 leads this result
GPQA Diamond (Vals)
Not directly comparable
MMLU-Pro (Vals)
Not directly comparable
MMMU-Pro
Claude Opus 4.6 leads this result
ERQA
Shared sourceGrok 4.20 leads this result
ScreenSpot Pro
Not directly comparable
MedXpertQA (MM)
Shared sourceGrok 4.20 leads this result
CharXiv
Not directly comparable
SimpleVQA
Not directly comparable
Claude Opus 4.6 has the higher public score estimate, 69 versus 67.13, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
Claude Opus 4.6 leads the public coding lane, 56.6 to 28.2, with Supported evidence for both models and non-overlapping 90% intervals.
Claude Opus 4.6 leads the public agentic tasks lane, 50.1 to 26.7, with Supported evidence for both models and non-overlapping 90% intervals.
For the stated presets, chat costs $0.0175 on Claude Opus 4.6 and $0.005 on Grok 4.20; repository review costs $0.325 and $0.118; the cache-heavy agent loop costs $1.35 and $0.5. Claude Opus 4.6 has no published cached-input rate, so cached tokens use its listed input rate. Grok 4.20 has no published cached-input rate, so cached tokens use its listed input rate.
Grok 4.20 has the larger documented context window: 2M, compared with 1M.
Last updated September 10, 2026
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