Chat turn cost
1K fresh input + 500 output tokens
GPT-4.1 nano
GPT-4.1 nano has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Claude Sonnet 5 vs GPT-4.1 nano
Updated September 29, 2026. Rank says Claude Sonnet 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
Share or export
Decision reading
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. Use the documented cost, context, and runtime rows instead. 0 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 0
- Claude Sonnet 5 only
- 26
- GPT-4.1 nano only
- 4
- Like-for-like categories
- 1 / 8
Supported: Claude Sonnet 5 · Estimated: GPT-4.1 nanoHow the comparison works
Which one for your work
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.
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
GPT-4.1 nano
GPT-4.1 nano has the lower estimated token cost for this stated workload. GPT-4.1 nano has no published cached-input rate, so cached tokens use its listed input rate.
Confidence: rate-fallback
Show secondary and unsupported calls
Repository review cost
50K fresh input + 3K output tokens
GPT-4.1 nano
GPT-4.1 nano has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-ratesCoding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
GPT-4.1 nano is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.
Confidence: limitedAgentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
GPT-4.1 nano is not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limitedLong documents
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
Confidence: documented
Which one for a specific job
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.
59.8Claude Sonnet 515.2GPT-4.1 nano
Directional only · BenchAlign v5.7
Claude Sonnet 5 scores higher, but at least one score rests on Estimated evidence or a different benchmark set. Directional only, no winner.
Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.
Same basis rules as the category table below
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
26 Claude Sonnet 5 only
4 GPT-4.1 nano only
1 category rests on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
Shape of the matched evidence
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.
Category results, on a stated basis
Each row shows the public-lane category score for both models: the BenchAlign v5.7 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.
Knowledge
Like-for-like- Claude Sonnet 5
- 64.5
- Supported · #26/168
- GPT-4.1 nano
- 25.2
- Supported · #161/168
- Basis
- BenchAlign v5.7 lane · 6 vs 2 public rows
- Reading
- Claude Sonnet 5 leads
Coding
Directional only- Claude Sonnet 5
- 59.8
- Supported · #19/142
- GPT-4.1 nano
- 15.2
- Estimated · #136/142
- Basis
- BenchAlign v5.7 lane · 11 vs 0 public rows
- Reading
- Directional only
Agentic
Not comparable- Claude Sonnet 5
- 64.6
- Supported · #12/117
- GPT-4.1 nano
- Not ranked
- Basis
- BenchAlign v5.7 lane · 7 vs 0 public rows
- Reading
- Not comparable
Reasoning
Not comparable- Claude Sonnet 5
- 78.7
- Unranked · 2 rankable rows
- GPT-4.1 nano
- 36.1
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Claude Sonnet 5
- 78.4
- #15/50
- GPT-4.1 nano
- 26.4
- Unranked · 1 rankable row
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- Claude Sonnet 5
- Not ranked
- GPT-4.1 nano
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Claude Sonnet 5
- Not ranked
- GPT-4.1 nano
- 34.6
- #109/124
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- Claude Sonnet 5
- Not ranked
- GPT-4.1 nano
- 25.4
- Unranked · 1 rankable row
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Not comparable
| Category | Claude Sonnet 5 | GPT-4.1 nano | Basis | Reading |
|---|---|---|---|---|
| Knowledge | 64.5Supported · #26/168 | 25.2Supported · #161/168 | Like-for-likeBenchAlign v5.7 lane · 6 vs 2 public rows | Claude Sonnet 5 leads |
| Coding | 59.8Supported · #19/142 | 15.2Estimated · #136/142 | Directional onlyBenchAlign v5.7 lane · 11 vs 0 public rows | Directional only |
| Agentic | 64.6Supported · #12/117 | Not ranked | Not comparableBenchAlign v5.7 lane · 7 vs 0 public rows | Not comparable |
| Reasoning | 78.7Unranked · 2 rankable rows | 36.1Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | 78.4#15/50 | 26.4Unranked · 1 rankable row | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | Not ranked | 34.6#109/124 | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | Not ranked | 25.4Unranked · 1 rankable row | 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 v5.7) 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.
Supported evidence per lane · bars run 0–100Methodology
What each workload costs
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.
Chat turn
1K fresh input + 500 output tokens
- Claude Sonnet 5
- $0.007
- Fits in one request
- GPT-4.1 nano
- $0.0003
- Fits in one request
GPT-4.1 nano has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- Claude Sonnet 5
- $0.13
- Fits in one request
- GPT-4.1 nano
- $0.0062
- Fits in one request
GPT-4.1 nano has the lower modeled cost
Costs use the listed standard API rates.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- Claude Sonnet 5
- $0.18
- Fits in one request
- GPT-4.1 nano
- $0.026
- Fits in one request
- Cached input priced at the published list-input rate
GPT-4.1 nano has the lower modeled cost
GPT-4.1 nano has no published cached-input rate, so cached tokens use its listed input rate.
Cached input falls back to the list input rate only where a cached rate is unpublished
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationClaude Sonnet 5GPT-4.1 nano
Context window
Maximum documented context; output-token limits may be lower.
Claude Sonnet 5
GPT-4.1 nano
1M
API model ID
Claude Sonnet 5
claude-sonnet-5
Anthropic model overviewGPT-4.1 nano
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Claude Sonnet 5
$0.2 per 1M cached input tokens
Claude API pricingGPT-4.1 nano
Not published
Documented inputs
Claude Sonnet 5
text, image
Anthropic model overviewGPT-4.1 nano
Not sourced
Documented outputs
Claude Sonnet 5
GPT-4.1 nano
Not sourced
Provider availability
Claude Sonnet 5
Generally Available · Claude API
Anthropic model overviewGPT-4.1 nano
Not sourced
Reasoning profile
Claude Sonnet 5
Reasoning
GPT-4.1 nano
Non-Reasoning
Weight access
Claude Sonnet 5
Proprietary
GPT-4.1 nano
Proprietary
License
Claude Sonnet 5
Proprietary
GPT-4.1 nano
Proprietary
Release date
Claude Sonnet 5
2026-06-30
GPT-4.1 nano
2025-04-14
If you already use one of these models
- Deployment change
- The models list different providers, so authentication, endpoint behavior, limits, and feature support may change.
- Quality signal
- The public evidence has no benchmark result shared by both models, so it does not support a quality verdict.
- Workload cost
- Repository review: $0.13 vs $0.0062. Cache-heavy agent loop: $0.18 vs $0.026.
- Context tradeoff
- Both models list 1M.
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, Claude Sonnet 5 or GPT-4.1 nano?
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. The page therefore keeps the decision tied to the specific documented workload.
Which is better for coding, Claude Sonnet 5 or GPT-4.1 nano?
Claude Sonnet 5 scores higher for coding on the public lane, 59.8 to 15.2. GPT-4.1 nano 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.
Which is better for agentic tasks, Claude Sonnet 5 or GPT-4.1 nano?
GPT-4.1 nano is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, Claude Sonnet 5 or GPT-4.1 nano?
For the stated presets, chat costs $0.007 on Claude Sonnet 5 and $0.0003 on GPT-4.1 nano; repository review costs $0.13 and $0.0062; the cache-heavy agent loop costs $0.18 and $0.026. GPT-4.1 nano has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, Claude Sonnet 5 or GPT-4.1 nano?
Both models list the same context window, 1M.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence30 rows
Agentic
Terminal-Bench 3.0
Claude Sonnet 514.6%SourceGPT-4.1 nano—Not directly comparable
Terminal-Bench 2.1
Claude Sonnet 580.4%SourceGPT-4.1 nano—Not directly comparable
BrowseComp
Claude Sonnet 584.7%SourceGPT-4.1 nano—Not directly comparable
HLE w/ tools
Claude Sonnet 557.4%SourceGPT-4.1 nano—Not directly comparable
OSWorld-Verified
Claude Sonnet 581.2%SourceGPT-4.1 nano—Not directly comparable
Terminal-Bench 2.1 (Vals)
Claude Sonnet 574.5%SourceGPT-4.1 nano—Not directly comparable
ApprenticeBench
Claude Sonnet 516%SourceGPT-4.1 nano—Not directly comparable
Coding
SWE-bench Verified
Claude Sonnet 585.2%SourceGPT-4.1 nano—Not directly comparable
SWE-bench Pro
Claude Sonnet 563.2%SourceGPT-4.1 nano—Not directly comparable
SWE Multilingual
Claude Sonnet 578.3%SourceGPT-4.1 nano—Not directly comparable
SWE Multimodal
Claude Sonnet 528.1%SourceGPT-4.1 nano—Not directly comparable
Terminal-Bench 2.1
Claude Sonnet 580.4%SourceGPT-4.1 nano—Not directly comparable
FrontierCode 1.1 Main
Claude Sonnet 542.7%SourceGPT-4.1 nano—Not directly comparable
cursorBench32
Claude Sonnet 561.5%SourceGPT-4.1 nano—Not directly comparable
VulcanBench CII v1
Claude Sonnet 589.2%SourceGPT-4.1 nano—Not directly comparable
LiveCodeBench (Vals)
Claude Sonnet 582.4%SourceGPT-4.1 nano—Not directly comparable
SWE-bench (Vals)
Claude Sonnet 579.6%SourceGPT-4.1 nano—Not directly comparable
cursorBench40
Claude Sonnet 534.1%SourceGPT-4.1 nano—Not directly comparable
Multimodal
Knowledge
HLE
Claude Sonnet 557.4%SourceGPT-4.1 nano—Not directly comparable
HLE w/o tools
Claude Sonnet 543.2%SourceGPT-4.1 nano—Not directly comparable
HLE-Verified
Claude Sonnet 531.0%SourceGPT-4.1 nano—Not directly comparable
LABBench2
Claude Sonnet 580.1%SourceGPT-4.1 nano—Not directly comparable
GPQA Diamond (Vals)
Claude Sonnet 588.9%SourceGPT-4.1 nano—Not directly comparable
MMLU-Pro (Vals)
Claude Sonnet 587.5%SourceGPT-4.1 nano—Not directly comparable
MMLU
Claude Sonnet 5—GPT-4.1 nano80.1%SourceNot directly comparable
GPQA
Claude Sonnet 5—GPT-4.1 nano50.3%SourceNot directly comparable
Instruction following
IFEval
Claude Sonnet 5—GPT-4.1 nano83.2%SourceNot directly comparable
Math
FrontierMath v2 (Tiers 1-3)
Claude Sonnet 5—GPT-4.1 nano1.034%SourceNot directly comparable
30 public results · 0 shared
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Last updated September 29, 2026