Updated October 7, 2026. Rank says Claude Haiku 5.5 is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
Both of these models will change. Get the price, version and retirement notices for the pair, each with its source. Follow model changes
Claude Haiku 5.5 has the higher public point estimate, 66.32 versus 64.02. Their conditional score ranges overlap. These ranges do not establish rank confidence. 1 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 Haiku 5.5
Claude Haiku 5.5 has the higher public coding point estimate, 60.9 to 49, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Tool use, computer use, and multi-step task completion
Claude Haiku 5.5
Claude Haiku 5.5 has the higher public agentic point estimate, 62.1 to 52.2, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
1K fresh input + 500 output tokens
Claude Haiku 5.5
Claude Haiku 5.5 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
Claude Haiku 5.5
Claude Haiku 5.5 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
50K fresh input + 3K output tokens
Claude Haiku 5.5
Claude Haiku 5.5 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
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 Haiku 5.5 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.
Only shared public evidence is shown. Sparse evidence stays a ruled list rather than being closed into a radar shape.
A shared-evidence shape is not available.
BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.
Each 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 Haiku 5.5 | DeepSeek V4 Pro 0813 | Basis | Reading |
|---|---|---|---|---|
| Agentic | 62.1Supported · #18/122 | 52.2Supported · #42/122 | Like-for-likeBenchAlign v5.8 lane · 2 vs 11 public rows | Claude Haiku 5.5 leads · intervals overlap |
| Coding | 60.9Supported · #20/146 | 49.0Supported · #45/146 | Like-for-likeBenchAlign v5.8 lane · 1 vs 15 public rows | Claude Haiku 5.5 leads · intervals overlap |
| Reasoning | 79.2Unranked · 2 rankable rows | 56.9Unranked · 4 rankable rows | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
| Multimodal | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Knowledge | Not ranked | 62.8Estimated · #37/174 | Not comparableBenchAlign v5.8 lane · 1 vs 8 public rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | Not ranked | 80.2Unranked · 4 rankable rows | Not comparableProvisional lane · 0 vs 1 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
Claude Haiku 5.5 has the lower modeled cost
Costs use the listed standard API rates.
50K fresh input + 3K output tokens
Claude Haiku 5.5 has the lower modeled cost
Costs use the listed standard API rates.
200K cached + 20K fresh input + 10K output tokens
Claude Haiku 5.5 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 Haiku 5.5
DeepSeek V4 Pro 0813
Claude Haiku 5.5
claude-haiku-5-5
Anthropic Claude Haiku 5.5 model documentationDeepSeek 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 Haiku 5.5
$0.01 per 1M cached input tokens
Claude API pricingDeepSeek V4 Pro 0813
$0.044 per 1M cached input tokens
DeepSeek: Models & PricingClaude Haiku 5.5
DeepSeek V4 Pro 0813
Claude Haiku 5.5
DeepSeek V4 Pro 0813
Claude Haiku 5.5
Generally Available · Claude API, Amazon Bedrock, Google Cloud, Microsoft Foundry, Claude Platform on AWS
Anthropic Claude Haiku 5.5 model documentationDeepSeek V4 Pro 0813
Generally Available · DeepSeek API
DeepSeek models and pricing, V4 Pro continuation footnoteClaude Haiku 5.5
Reasoning
DeepSeek V4 Pro 0813
Reasoning
Claude Haiku 5.5
Proprietary
DeepSeek V4 Pro 0813
Open Weight
Claude Haiku 5.5
Proprietary
DeepSeek V4 Pro 0813
Open Weight
Claude Haiku 5.5
2026-10-07
DeepSeek V4 Pro 0813
2026-08-13
Claude Haiku 5.5 has the higher public point estimate, 66.32 versus 64.02. Their conditional score ranges overlap. These ranges do not establish rank confidence. The page therefore keeps the decision tied to the specific documented workload.
Claude Haiku 5.5 has the higher public coding point estimate, 60.9 to 49, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Claude Haiku 5.5 has the higher public agentic tasks point estimate, 62.1 to 52.2, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
For the stated presets, chat costs $0.00035 on Claude Haiku 5.5 and $0.0033 on DeepSeek V4 Pro 0813; repository review costs $0.0065 and $0.07788; the cache-heavy agent loop costs $0.009 and $0.0748. Costs use the listed standard API rates.
Both models list the same context window, 1M.
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Terminal-Bench 4.0
Not directly comparable
HLE w/ tools
DeepSeek V4 Pro 0813 leads this result
Terminal-Bench 2.0
Not directly comparable
Terminal-Bench 2.1
Not directly comparable
BrowseComp
Not directly comparable
MCP Atlas
Not directly comparable
Toolathlon
Not directly comparable
CyberGym
Not directly comparable
Toolathlon-Verified
Not directly comparable
Agents' Last Exam
Not directly comparable
AutomationBench
Not directly comparable
Terminal-Bench 2.1 (Vals)
Not directly comparable
FrontierCode 1.1 Main
Not directly comparable
LiveCodeBench Pass@1-COT
Not directly comparable
Codeforces
Not directly comparable
SWE-bench Verified
Not directly comparable
SWE-bench Pro
Not directly comparable
SWE Multilingual
Not directly comparable
Terminal-Bench 2.0
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
LiveCodeBench (Vals)
Not directly comparable
SWE-bench (Vals)
Not directly comparable
MRCR 1M
Not directly comparable
CorpusQA 1M
Not directly comparable
ARC-AGI-1
Not directly comparable
ARC-AGI-2
Not directly comparable
Chartography (no tools)
Not directly comparable
HLE w/o tools
Not directly comparable
MMLU-Pro
Not directly comparable
SimpleQA
Not directly comparable
Chinese-SimpleQA
Not directly comparable
GPQA
Not directly comparable
GPQA-D
Not directly comparable
HLE
Not directly comparable
GPQA Diamond (Vals)
Not directly comparable
MMLU-Pro (Vals)
Not directly comparable
HMMT Feb 2026
Not directly comparable
IMOAnswerBench
Not directly comparable
Apex
Not directly comparable
Apex Shortlist
Not directly comparable
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Last updated October 7, 2026
