Coding
Like-for-like- Claude Opus 4.5
- 71.7
- Claude Opus 4.8
- 81.1
- Weighted basis
- 2 vs 2 rows
- Reading
- Claude Opus 4.8 leads
Five or fewer confirmed AI changes, with original sources, on mornings when something changed.A free source-linked morning brief.
Start the free Radar BriefUpdated August 26, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
Claude Opus 4.8 has the higher public score estimate, 76.45 versus 63.78, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
14 results are shared. Category rows based on 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 Opus 4.8
Claude Opus 4.8 leads on the same 2 weighted benchmark rows.
Confidence: limited
Prompts that approach the documented context limit
Claude Opus 4.8
Claude Opus 4.8 has the larger documented context window.
Confidence: documented
Tool use, computer use, and multi-step task completion
Not enough matched evidence
The category averages use different weighted benchmark sets, so they are directional rather than like-for-like.
Confidence: limited
1K fresh input + 500 output tokens
No clear pick
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-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 Opus 4.5 does not fit this workload in one request. Claude Opus 4.5 has no published cached-input rate, so cached tokens use its listed input rate. Claude Opus 4.8 has no published cached-input rate, so cached tokens use its listed input rate.
Confidence: rate-fallback
50K fresh input + 3K output tokens
No clear pick
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
4 categories use different evidence sets. Those rows remain visible for coverage context but do not name a winner.
Each row states whether both averages use the same weighted benchmark set. Directional and not-comparable rows remain visible, but they never receive a winner in this template.
| Category | Claude Opus 4.5 | Claude Opus 4.8 | Weighted basis | Reading |
|---|---|---|---|---|
| Coding | 71.7 | 81.1 | Like-for-like2 vs 2 rows | Claude Opus 4.8 leads |
| Agentic | 62.6 | 80.3 | Directional only2 vs 3 rows | Directional only |
| Knowledge | 58.1 | 62.7 | Directional only4 vs 2 rows | Directional only |
| Math | 57.5 | 53.9 | Directional only4 vs 3 rows | Directional only |
| Multimodal | 69.9 | 77.0 | Directional only2 vs 2 rows | Directional only |
| Reasoning | 64.4 | 72.1 | Not comparable1 vs 1 rows | Not comparable |
| Multilingual | 85.7 | Not measured | Not comparable1 vs 0 rows | Not comparable |
| Instruction following | 69.5 | Not measured | Not comparable2 vs 0 rows | Not comparable |
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.
HLE
Knowledge
FrontierMath v2 (Tier 4)
Math
FrontierMath v2 (Tiers 1-3)
Math
CharXiv
Multimodal
OSWorld-Verified
Agentic
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
Modeled costs are equal
Costs use the listed standard API rates.
50K fresh input + 3K output tokens
Modeled costs are equal
Costs use the listed standard API rates.
200K cached + 20K fresh input + 10K output tokens
Claude Opus 4.5 does not fit this workload in one request. Claude Opus 4.5 has no published cached-input rate, so cached tokens use its listed input rate. 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 4.5
200K
Claude Opus 4.8
Claude Opus 4.5
Not sourced
Claude Opus 4.8
claude-opus-4-8
Anthropic model overviewA missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Claude Opus 4.5
Not published
Claude Opus 4.8
Not published
Claude Opus 4.5
Not sourced
Claude Opus 4.8
text, image
Anthropic model overviewClaude Opus 4.5
Not sourced
Claude Opus 4.8
Claude Opus 4.5
Not sourced
Claude Opus 4.8
Generally Available · Claude API
Anthropic model overviewClaude Opus 4.5
Non-Reasoning
Claude Opus 4.8
Reasoning
Claude Opus 4.5
Proprietary
Claude Opus 4.8
Proprietary
Claude Opus 4.5
Proprietary
Claude Opus 4.8
Proprietary
Claude Opus 4.5
2025-11-01
Claude Opus 4.8
2026-05-28
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 4.8 leads this result
OSWorld-Verified
Claude Opus 4.8 leads this result
OSWorld
Not directly comparable
Claw-Eval
Not directly comparable
QwenClawBench
Not directly comparable
τ³-bench results
Not directly comparable
VITA-Bench
Not directly comparable
DeepPlanning
Not directly comparable
Toolathlon
Claude Opus 4.8 leads this result
MCP Atlas
Claude Opus 4.8 leads this result
MCP-Tasks
Not directly comparable
WideResearch
Not directly comparable
CyberGym
Not directly comparable
Gert Labs
Shared sourceClaude Opus 4.8 leads this result
JobBench
Not directly comparable
Terminal-Bench 3.0
Not directly comparable
BrowseComp
Not directly comparable
DeepSearchQA
Not directly comparable
Finance Agent v2
Not directly comparable
ResearchClawBench
Not directly comparable
OSWorld 2.0
Not directly comparable
SWE-bench Verified
Claude Opus 4.8 leads this result
LiveCodeBench v6
Not directly comparable
SWE-bench Pro
Claude Opus 4.8 leads this result
SWE Multilingual
Claude Opus 4.8 leads this result
NL2Repo
Not directly comparable
SWE Multimodal
Not directly comparable
Terminal-Bench 2.0
Not directly comparable
cursorBench31
Not directly comparable
cursorBench32
Not directly comparable
FrontierCode 1.1 Main
Not directly comparable
LongBench v2
Not directly comparable
AI-Needle
Not directly comparable
ARC-AGI-2
Not directly comparable
ARC-AGI-3
Not directly comparable
GPQA
Claude Opus 4.8 leads this result
SuperGPQA
Not directly comparable
MMLU-Pro
Not directly comparable
MMLU-Redux
Not directly comparable
C-Eval
Not directly comparable
HLE
Claude Opus 4.8 leads this result
GPQA-D
Not directly comparable
HLE w/o tools
Not directly comparable
AIME26
Not directly comparable
HMMT Feb 2025
Not directly comparable
HMMT Nov 2025
Not directly comparable
HMMT Feb 2026
Not directly comparable
MMAnswerBench
Not directly comparable
FrontierMath v2 (Tiers 1-3)
Shared sourceClaude Opus 4.8 leads this result
FrontierMath v2 (Tier 4)
Shared sourceClaude Opus 4.8 leads this result
USAMO 2026
Not directly comparable
MMMU-Pro
Not directly comparable
MathVision
Not directly comparable
CharXiv
Claude Opus 4.8 leads this result
VideoMMMU
Not directly comparable
ScreenSpot Pro
Claude Opus 4.8 leads this result
V*
Not directly comparable
OfficeQA Pro
Not directly comparable
CharXiv w/o tools
Not directly comparable
Claude Opus 4.8 has the higher public score estimate, 76.45 versus 63.78, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
Claude Opus 4.8 leads the like-for-like coding comparison across 2 shared weighted benchmark rows.
The current agentic tasks averages use different weighted benchmark sets, so BenchLM does not name a winner from them. 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 4.5 and $0.0175 on Claude Opus 4.8; repository review costs $0.325 and $0.325; the cache-heavy agent loop costs $1.35 and $1.35. Claude Opus 4.5 does not fit this workload in one request. Claude Opus 4.5 has no published cached-input rate, so cached tokens use its listed input rate. Claude Opus 4.8 has no published cached-input rate, so cached tokens use its listed input rate.
Claude Opus 4.8 has the larger documented context window: 1M, compared with 200K.
Last updated August 26, 2026
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