Chat turn cost
1K fresh input + 500 output tokens
Step 5 Preview
Step 5 Preview has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Updated November 12, 2026. Rank says Claude Sonnet 5.5 is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
Claude Sonnet 5.5 has the higher public point estimate, 83.07 versus 55.48. Their conditional score ranges do not overlap. These ranges do not establish rank confidence. 8 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
Claude Sonnet 5.5: $2 input / $10 output
Step 5 Preview: $1 input / $2.70 output
Claude Sonnet 5.5: 1M
Step 5 Preview: 1M
Claude Sonnet 5.5: September 28, 2026
Step 5 Preview: September 20, 2026
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.
1K fresh input + 500 output tokens
Step 5 Preview
Step 5 Preview 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
Step 5 Preview
Step 5 Preview has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
50K fresh input + 3K output tokens
Step 5 Preview
Step 5 Preview has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Step 5 Preview is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Step 5 Preview is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
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.
Directional only · BenchAlign v5.8
Claude Sonnet 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.
The LLM Selector reads what you need a model for and returns a shortlist, ranked in the same order the job pages publish.
We keep what you type here, drafts included, for 180 days, and after that only the job and requirements read from it. Nothing is tied to your IP address or account. Leave out names and other personal details. Privacy
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
4 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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.
OfficeQA ProMultimodal
Normalized gap 5.3Each row shows the public-lane category score for both models: the BenchAlign v5.8 lane for agentic, coding, reasoning, multimodal, 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 Sonnet 5.5 | Step 5 Preview | Basis | Reading |
|---|---|---|---|---|
| Knowledge | 82.1Supported · #6/180 | 61.2Supported · #45/180 | Like-for-likeBenchAlign v5.8 lane · 17 vs 2 public rows | Claude Sonnet 5.5 leads |
| Agentic | 72.7Supported · #9/135 | 54.2Estimated · #51/135 | Directional onlyBenchAlign v5.8 lane · 19 vs 13 public rows | Directional only |
| Coding | 81.0Supported · #4/162 | 49.1Estimated · #52/162 | Directional onlyBenchAlign v5.8 lane · 12 vs 6 public rows | Directional only |
| Reasoning | 77.0Supported · #6/160 | 53.2Estimated · #49/160 | Directional onlyBenchAlign v5.8 lane · 0 vs 1 public rows | Directional only |
| Multimodal | 78.9Estimated · #7/67 | 60.2Estimated · #37/67 | Directional onlyBenchAlign v5.8 lane · 10 vs 2 public rows | Directional only |
| 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 | 89.7Unranked · 4 rankable rows | Not ranked | Not comparableProvisional lane · 2 vs 0 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, reasoning, multimodal, 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
Step 5 Preview has the lower modeled cost
Costs use the listed standard API rates.
50K fresh input + 3K output tokens
Step 5 Preview has the lower modeled cost
Costs use the listed standard API rates.
200K cached + 20K fresh input + 10K output tokens
Step 5 Preview has the lower modeled cost
Costs use the listed standard API rates.
Enter a month of tokens to price it at both models’ listed API rates.
Standard listed rates, no batch or volume discounts.
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 Sonnet 5.5
Step 5 Preview
Claude Sonnet 5.5
claude-sonnet-5-5
Anthropic Claude Sonnet 5.5 model documentationStep 5 Preview
step-5-preview
StepFun Step 5 Preview model documentationA missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Claude Sonnet 5.5
$0.1 per 1M cached input tokens
Claude API pricingStep 5 Preview
$0.05 per 1M cached input tokens
StepFun pricing and rate limitsClaude Sonnet 5.5
Step 5 Preview
text, image, video
StepFun Step 5 Preview model documentationClaude Sonnet 5.5
Step 5 Preview
Claude Sonnet 5.5
Generally Available · Claude API, Amazon Bedrock, Google Cloud, Microsoft Foundry, Claude Platform on AWS
Anthropic Claude Sonnet 5.5 model documentationStep 5 Preview
Preview · StepFun Open Platform API, Hugging Face, self-hosted inference
StepFun Step 5 Preview model documentationClaude Sonnet 5.5
Reasoning
Step 5 Preview
Reasoning
Claude Sonnet 5.5
Proprietary
Step 5 Preview
Open Weight
Claude Sonnet 5.5
Proprietary
Step 5 Preview
Open Weight
Claude Sonnet 5.5
2026-09-28
Step 5 Preview
2026-09-20
Claude Sonnet 5.5 has the higher public point estimate, 83.07 versus 55.48. Their conditional score ranges do not overlap. These ranges do not establish rank confidence. The page therefore keeps the decision tied to the specific documented workload.
Claude Sonnet 5.5 scores higher for coding on the public lane, 81 to 49.1. Step 5 Preview is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like. BenchLM does not name a winner for coding from a directional reading; read the shared benchmark rows directly and test the models on the same task set.
Claude Sonnet 5.5 scores higher for agentic tasks on the public lane, 72.7 to 54.2. Step 5 Preview is scored on Estimated evidence for agentic tasks, so the reading is directional rather than like-for-like. BenchLM does not name a winner for agentic tasks from a directional reading; read the shared benchmark rows directly and test the models on the same task set.
For the stated presets, chat costs $0.007 on Claude Sonnet 5.5 and $0.00235 on Step 5 Preview; repository review costs $0.13 and $0.0581; the cache-heavy agent loop costs $0.16 and $0.057. Costs use the listed standard API rates.
Both models list the same context window, 1M.
Estimates at 50,000 req/day · 1000 tokens/req average.
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Terminal-Bench 4.1
Not directly comparable
Terminal-Bench 4.0
Claude Sonnet 5.5 leads this result
HLE w/ tools
Not directly comparable
DRACO
Claude Sonnet 5.5 leads this result
Terminal-Bench-Science 0.1
Not directly comparable
LAB all-pass (Harvey held-out)
Not directly comparable
LAB criterion-pass (Harvey held-out)
Not directly comparable
Toolathlon Verified Pass@3
Not directly comparable
Toolathlon Verified Pass³
Not directly comparable
Toolathlon Verified avg. turns
Not directly comparable
AutomationBench (Zapier 1.0.6)
Not directly comparable
Toolathlon-Verified
Claude Sonnet 5.5 leads this result
Hermes Index
Shared sourceClaude Sonnet 5.5 leads this result
Hermes Bench
Shared sourceClaude Sonnet 5.5 leads this result
MazeBench
Not directly comparable
OSWorld 2.1
Not directly comparable
OSWorld 2.1 Offline
Not directly comparable
Binary Exploitation Benchmark
Not directly comparable
Terminal-Bench 2.1 (Vals)
Not directly comparable
Terminal-Bench 2.1
Not directly comparable
CyberGym
Not directly comparable
AutomationBench
Not directly comparable
MCP Atlas
Not directly comparable
JobBench
Not directly comparable
APEX-Agents
Not directly comparable
BrowseComp
Not directly comparable
Agents' Last Exam
Not directly comparable
FrontierCode 1.1 Main
Not directly comparable
CursorBench 4.0
Not directly comparable
SWE-bench Pro
Not directly comparable
SWE Multilingual
Not directly comparable
SWE Multimodal
Not directly comparable
DeepSWE
Claude Sonnet 5.5 leads this result
FrontierCode 1.1 Extended
Not directly comparable
ProgramBench
Step 5 Preview leads this result
FrontierSWE v2
Not directly comparable
Bug Hunt Bench
Not directly comparable
FrontierCode 2.0
Not directly comparable
VulcanBench Frontier v4
Not directly comparable
Terminal-Bench 2.1
Not directly comparable
SciCode
Not directly comparable
sweMarathon
Not directly comparable
MLS-Bench Lite
Not directly comparable
CritPt
Not directly comparable
ExtractBench Pro
Not directly comparable
ExtractBench (LlamaIndex)
Not directly comparable
ParseBench
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
Biomedical image analysis
Not directly comparable
OfficeQA
Not directly comparable
OfficeQA Pro
Claude Sonnet 5.5 leads this result
MMMU-Pro
Not directly comparable
HealthBench (raw)
Not directly comparable
HealthBench (length-adjusted)
Not directly comparable
HealthBench Professional (raw)
Not directly comparable
HealthBench Professional
Not directly comparable
BioMysteryBench (human-solvable)
Not directly comparable
BioMysteryBench (human-difficult)
Not directly comparable
SpatialBench Verified
Not directly comparable
SingleCellBench
Not directly comparable
Protein Design
Not directly comparable
Morphology-to-molecule matching
Not directly comparable
Medicinal chemistry
Not directly comparable
Protein Design library ranking
Not directly comparable
De novo protein-binder design
Not directly comparable
Protocols (troubleshooting)
Not directly comparable
Protocols (understanding)
Not directly comparable
HLE w/o tools
Not directly comparable
PhysicianBench
Not directly comparable
GPQA-D
Not directly comparable
HLE
Not directly comparable
ArXivMath Aug. 2026 (no tools)
Not directly comparable
ArXivMath Aug. 2026 (tools)
Not directly comparable
FrontierMath v2 (Tiers 1-3)
Not directly comparable
FrontierMath v2 (Tier 4)
Not directly comparable
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Last updated November 12, 2026