Agentic
Like-for-like- Claude Opus 4.8
- 62.5
- Supported · #14/152
- DeepSeek V4 Pro 0813
- 56.5
- Supported · #33/152
- Basis
- BenchAlign lane · 12 vs 11 public rows
- Reading
- Claude Opus 4.8 leads · intervals overlap
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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 4.8 has the higher public score estimate, 72.22 versus 66.38, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
16 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 4.8
Claude Opus 4.8 leads on the public coding lane, 66.4 to 51.9, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limited
Tool use, computer use, and multi-step task completion
Claude Opus 4.8
Claude Opus 4.8 leads on the public agentic lane, 62.5 to 56.5, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limited
1K fresh input + 500 output tokens
DeepSeek V4 Pro 0813
DeepSeek V4 Pro 0813 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
DeepSeek V4 Pro 0813
DeepSeek V4 Pro 0813 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.
Confidence: rate-fallback
50K fresh input + 3K output tokens
DeepSeek V4 Pro 0813
DeepSeek V4 Pro 0813 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
Confidence: documented
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 4.8 | DeepSeek V4 Pro 0813 | Basis | Reading |
|---|---|---|---|---|
| Agentic | 62.5Supported · #14/152 | 56.5Supported · #33/152 | Like-for-likeBenchAlign lane · 12 vs 11 public rows | Claude Opus 4.8 leads · intervals overlap |
| Coding | 66.4Supported · #12/151 | 51.9Supported · #49/151 | Like-for-likeBenchAlign lane · 10 vs 15 public rows | Claude Opus 4.8 leads · intervals overlap |
| Reasoning | 55.5#18/20 | 60.3#15/20 | Directional onlyProvisional lane · 2 vs 0 weighted rows | Directional only |
| Knowledge | 71.4Supported · #9/183 | 59.8Estimated · #38/183 | Directional onlyBenchAlign lane · 6 vs 8 public rows | Directional only |
| Math | 65.4#2/7 | 80.4Unranked · 4 rankable rows | Not comparableProvisional lane · 3 vs 1 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | 87.8#3/48 | Not ranked | Not comparableProvisional lane · 2 vs 0 weighted rows | Not comparable |
| Instruction following | 75.4#61/123 | 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.
HLE
Knowledge
SWE-bench Pro
Coding
SWE Multilingual
Coding
SWE-bench Verified
Coding
Terminal-Bench 2.0
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
DeepSeek V4 Pro 0813 has the lower modeled cost
Costs use the listed standard API rates.
50K fresh input + 3K output tokens
DeepSeek V4 Pro 0813 has the lower modeled cost
Costs use the listed standard API rates.
200K cached + 20K fresh input + 10K output tokens
DeepSeek V4 Pro 0813 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
DeepSeek V4 Pro 0813
Claude Opus 4.8
claude-opus-4-8
Anthropic model overviewDeepSeek V4 Pro 0813
deepseek-v4-pro
DeepSeek models and pricingA missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Claude Opus 4.8
Not published
DeepSeek V4 Pro 0813
$0.003625 per 1M cached input tokens
Claude Opus 4.8
text, image
Anthropic model overviewDeepSeek V4 Pro 0813
Claude Opus 4.8
DeepSeek V4 Pro 0813
Claude Opus 4.8
Generally Available · Claude API
Anthropic model overviewDeepSeek V4 Pro 0813
Generally Available · DeepSeek API
DeepSeek-V4.1-Flash releaseClaude Opus 4.8
Reasoning
DeepSeek V4 Pro 0813
Reasoning
Claude Opus 4.8
Proprietary
DeepSeek V4 Pro 0813
Proprietary
Claude Opus 4.8
Proprietary
DeepSeek V4 Pro 0813
Proprietary
Claude Opus 4.8
2026-05-28
DeepSeek V4 Pro 0813
2026-08-13
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
Not directly comparable
Terminal-Bench 2.0
Claude Opus 4.8 leads this result
BrowseComp
Claude Opus 4.8 leads this result
DeepSearchQA
Not directly comparable
OSWorld-Verified
Not directly comparable
Finance Agent v2
Not directly comparable
MCP Atlas
Claude Opus 4.8 leads this result
Toolathlon
Claude Opus 4.8 leads this result
Gert Labs
Not directly comparable
ResearchClawBench
Not directly comparable
OSWorld 2.0
Not directly comparable
Terminal-Bench 2.1 (Vals)
Claude Opus 4.8 leads this result
Terminal-Bench 2.1
Not directly comparable
HLE w/ tools
Not directly comparable
CyberGym
Not directly comparable
Toolathlon-Verified
Not directly comparable
Agents' Last Exam
Not directly comparable
AutomationBench
Not directly comparable
SWE-bench Verified
Claude Opus 4.8 leads this result
SWE-bench Pro
Claude Opus 4.8 leads this result
SWE Multilingual
Claude Opus 4.8 leads this result
SWE Multimodal
Not directly comparable
Terminal-Bench 2.0
Claude Opus 4.8 leads this result
cursorBench31
Not directly comparable
cursorBench32
Not directly comparable
FrontierCode 1.1 Main
Not directly comparable
LiveCodeBench (Vals)
Claude Opus 4.8 leads this result
SWE-bench (Vals)
DeepSeek V4 Pro 0813 leads this result
LiveCodeBench Pass@1-COT
Not directly comparable
Codeforces
Not directly comparable
Vibe Code Bench
Not directly comparable
Terminal-Bench 2.1
Not directly comparable
NL2Repo
Not directly comparable
DeepSWE
Not directly comparable
DSBench-FullStack
Not directly comparable
DSBench-Hard
Not directly comparable
OpenHarmony Bench
Not directly comparable
ARC-AGI-2
Not directly comparable
ARC-AGI-3
Not directly comparable
MRCR 1M
Not directly comparable
CorpusQA 1M
Not directly comparable
GPQA
Claude Opus 4.8 leads this result
GPQA-D
Claude Opus 4.8 leads this result
HLE
Claude Opus 4.8 leads this result
HLE w/o tools
Not directly comparable
GPQA Diamond (Vals)
Tie
MMLU-Pro (Vals)
Claude Opus 4.8 leads this result
MMLU-Pro
Not directly comparable
SimpleQA
Not directly comparable
Chinese-SimpleQA
Not directly comparable
USAMO 2026
Not directly comparable
FrontierMath v2 (Tiers 1-3)
Not directly comparable
FrontierMath v2 (Tier 4)
Not directly comparable
HMMT Feb 2026
Not directly comparable
IMOAnswerBench
Not directly comparable
Apex
Not directly comparable
Apex Shortlist
Not directly comparable
INCLUDE
Not directly comparable
OfficeQA Pro
Not directly comparable
ScreenSpot Pro
Not directly comparable
CharXiv
Not directly comparable
CharXiv w/o tools
Not directly comparable
Claude Opus 4.8 has the higher public score estimate, 72.22 versus 66.38, 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 public coding lane, 66.4 to 51.9, with Supported evidence for both models, although the 90% intervals overlap.
Claude Opus 4.8 leads the public agentic tasks lane, 62.5 to 56.5, with Supported evidence for both models, although the 90% intervals overlap.
For the stated presets, chat costs $0.0175 on Claude Opus 4.8 and $0.00087 on DeepSeek V4 Pro 0813; repository review costs $0.325 and $0.02436; the cache-heavy agent loop costs $1.35 and $0.01812. Claude Opus 4.8 has no published cached-input rate, so cached tokens use its listed input rate.
Both models list the same context window, 1M.
Last updated September 10, 2026
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