Agentic
Like-for-like- Claude Opus 5
- 77.4
- Supported · #2/151
- Grok 4.5
- 62.9
- Supported · #17/151
- Basis
- BenchAlign lane · 19 vs 4 public rows
- Reading
- Claude Opus 5 leads
Five or fewer confirmed AI changes, with original sources, on mornings when something changed.A free source-linked morning brief.
See the free Radar BriefUpdated September 4, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
Claude Opus 5 has the higher public score, 80.66 versus 68.64, and the 90% score intervals do not overlap.
12 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 5
Claude Opus 5 leads on the public coding lane, 75.6 to 62.6, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limited
Tool use, computer use, and multi-step task completion
Claude Opus 5
Claude Opus 5 leads on the public agentic lane, 77.4 to 62.9, with Supported evidence for both models and non-overlapping 90% intervals.
Confidence: stronger
Prompts that approach the documented context limit
Claude Opus 5
Claude Opus 5 has the larger documented context window.
Confidence: documented
1K fresh input + 500 output tokens
Grok 4.5
Grok 4.5 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 4.5
Grok 4.5 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
50K fresh input + 3K output tokens
Grok 4.5
Grok 4.5 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.
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 5 | Grok 4.5 | Basis | Reading |
|---|---|---|---|---|
| Agentic | 77.4Supported · #2/151 | 62.9Supported · #17/151 | Like-for-likeBenchAlign lane · 19 vs 4 public rows | Claude Opus 5 leads |
| Coding | 75.6Supported · #3/183 | 62.6Supported · #17/183 | Like-for-likeBenchAlign lane · 16 vs 7 public rows | Claude Opus 5 leads · intervals overlap |
| Reasoning | 75.6#13/22 | 44.3#22/22 | Like-for-likeProvisional lane · 2 vs 2 weighted rows | Claude Opus 5 leads |
| Knowledge | 82.1Supported · #3/181 | 70.0Supported · #14/181 | Like-for-likeBenchAlign lane · 19 vs 2 public rows | Claude Opus 5 leads · intervals overlap |
| Math | Not ranked | Not ranked | 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 |
| Multimodal | 88.7#2/48 | 78.4Unranked · 1 rankable row | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Instruction following | 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) 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.
ARC-AGI-2
Reasoning
ARC-AGI-3
Reasoning
SWE-bench Pro
Coding
SWE Multilingual
Coding
cursorBench32
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 4.5 has the lower modeled cost
Costs use the listed standard API rates.
50K fresh input + 3K output tokens
Grok 4.5 has the lower modeled cost
Costs use the listed standard API rates.
200K cached + 20K fresh input + 10K output tokens
Grok 4.5 has the lower modeled cost
Costs use the listed standard API rates.
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 5
Grok 4.5
500K
Claude Opus 5
claude-opus-5
Anthropic model overviewGrok 4.5
Not sourced
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Claude Opus 5
$0.5 per 1M cached input tokens
Claude API pricingGrok 4.5
$0.3 per 1M cached input tokens
Claude Opus 5
text, image
Anthropic model overviewGrok 4.5
Not sourced
Claude Opus 5
Grok 4.5
Not sourced
Claude Opus 5
Generally Available · Claude API
Anthropic model overviewGrok 4.5
Not sourced
Claude Opus 5
Reasoning
Grok 4.5
Reasoning
Claude Opus 5
Proprietary
Grok 4.5
Proprietary
Claude Opus 5
Proprietary
Grok 4.5
Proprietary
Claude Opus 5
2026-07-24
Grok 4.5
2026-07-08
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 3.0
Shared sourceClaude Opus 5 leads this result
BrowseComp
Not directly comparable
HLE w/ tools
Not directly comparable
DeepSearchQA
Not directly comparable
DRACO
Not directly comparable
BrowseComp (10-agent, prerelease)
Not directly comparable
OSWorld 2.0
Not directly comparable
MCP Atlas
Not directly comparable
MCP-Atlas claim coverage
Not directly comparable
LAB all-pass (Anthropic harness)
Not directly comparable
LAB criterion-pass (Anthropic harness)
Not directly comparable
LAB all-pass (Harvey held-out)
Not directly comparable
LAB criterion-pass (Harvey held-out)
Not directly comparable
Toolathlon-Verified
Not directly comparable
Toolathlon Verified Pass@3
Not directly comparable
Toolathlon Verified Pass³
Not directly comparable
Toolathlon Verified avg. turns
Not directly comparable
AutomationBench
Not directly comparable
Terminal-Bench 2.1 (Vals)
Claude Opus 5 leads this result
Terminal-Bench 2.0
Not directly comparable
deepSwe
Not directly comparable
Bug Hunt Bench
Not directly comparable
SWE-bench Verified
Not directly comparable
SWE-bench Pro
Claude Opus 5 leads this result
SWE Multilingual
Claude Opus 5 leads this result
SWE Multimodal
Not directly comparable
deepSwe
Not directly comparable
FrontierCode 1.1 Main
Not directly comparable
FrontierCode 1.1 Extended
Not directly comparable
FrontierSWE v2
Not directly comparable
ProgramBench (episode 1)
Not directly comparable
ProgramBench
Not directly comparable
cursorBench32
Shared sourceClaude Opus 5 leads this result
VulcanBench v3
Grok 4.5 leads this result
VulcanBench CII v1
Not directly comparable
LiveCodeBench (Vals)
Claude Opus 5 leads this result
SWE-bench (Vals)
Claude Opus 5 leads this result
Terminal-Bench 2.0
Not directly comparable
HLE
Not directly comparable
HLE w/o tools
Not directly comparable
HLE-Verified
Not directly comparable
LABBench2
Not directly comparable
HealthBench (raw)
Not directly comparable
HealthBench (length-adjusted)
Not directly comparable
HealthBench Professional
Not directly comparable
HealthBench Professional (raw)
Not directly comparable
BioMysteryBench (human-solvable)
Not directly comparable
BioMysteryBench (human-difficult)
Not directly comparable
SpatialBench Verified
Not directly comparable
SingleCellBench
Not directly comparable
ProteinGym Hard
Not directly comparable
Protein Design
Not directly comparable
Organic chemistry V2
Not directly comparable
Protocols (troubleshooting)
Not directly comparable
Protocols (understanding)
Not directly comparable
GPQA Diamond (Vals)
Claude Opus 5 leads this result
MMLU-Pro (Vals)
Claude Opus 5 leads this result
IMO 2026
Not directly comparable
RiemannBench (no tools)
Not directly comparable
RiemannBench (tools)
Not directly comparable
ArXivMath Jun. 2026 (no tools)
Not directly comparable
ArXivMath Jun. 2026 (tools)
Not directly comparable
Chartography (no tools)
Not directly comparable
Chartography (tools)
Not directly comparable
BenchCAD Vision2Code (no tools)
Not directly comparable
BenchCAD Vision2Code (tools)
Not directly comparable
GDP.pdf (no tools)
Not directly comparable
GDP.pdf (tools)
Not directly comparable
OfficeQA
Not directly comparable
OfficeQA Pro
Not directly comparable
Claude Opus 5 has the higher public score, 80.66 versus 68.64, and the 90% score intervals do not overlap. That is the clearest overall quality signal in the current public data.
Claude Opus 5 leads the public coding lane, 75.6 to 62.6, with Supported evidence for both models, although the 90% intervals overlap.
Claude Opus 5 leads the public agentic tasks lane, 77.4 to 62.9, with Supported evidence for both models and non-overlapping 90% intervals.
For the stated presets, chat costs $0.0175 on Claude Opus 5 and $0.005 on Grok 4.5; repository review costs $0.325 and $0.118; the cache-heavy agent loop costs $0.45 and $0.16. Costs use the listed standard API rates.
Claude Opus 5 has the larger documented context window: 1M, compared with 500K.
Last updated September 4, 2026
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