Coding
Like-for-like- GPT-4.1
- 38.8
- Supported · #143/183
- GPT-5.5
- 67.7
- Supported · #8/183
- Basis
- BenchAlign lane · 1 vs 9 public rows
- Reading
- GPT-5.5 leads
GPT-4.1 vs GPT-5.5
Updated September 4, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
GPT-5.5 has the higher public score, 73.27 versus 43.81, and the 90% score intervals do not overlap.
4 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
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.
Coding work
Code generation, repair, and software-engineering tasks
GPT-5.5
GPT-5.5 leads on the public coding lane, 67.7 to 38.8, with Supported evidence for both models and non-overlapping 90% intervals.
Confidence: stronger
Chat turn cost
1K fresh input + 500 output tokens
GPT-4.1
GPT-4.1 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
GPT-5.5
GPT-5.5 has the lower estimated token cost for this stated workload. GPT-4.1 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
GPT-4.1 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
GPT-4.1 is not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limited
Long documents
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
Confidence: documented
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
3 GPT-4.1 only
4 Shared
34 GPT-5.5 only
- Shared results
- 4
- GPT-4.1 only
- 3
- GPT-5.5 only
- 34
- Like-for-like categories
- 2 / 8
1 category rests on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
Category results, on a stated basis
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.
Knowledge
Like-for-like- GPT-4.1
- 40.7
- Supported · #135/181
- GPT-5.5
- 73.3
- Supported · #7/181
- Basis
- BenchAlign lane · 2 vs 6 public rows
- Reading
- GPT-5.5 leads
Instruction following
Directional only- GPT-4.1
- 50.3
- #80/120
- GPT-5.5
- 92.9
- #7/120
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Directional only
Agentic
Not comparable- GPT-4.1
- Not ranked
- GPT-5.5
- 63.9
- Supported · #15/151
- Basis
- BenchAlign lane · 1 vs 13 public rows
- Reading
- Not comparable
Reasoning
Not comparable- GPT-4.1
- 67.2
- Unranked · 2 rankable rows
- GPT-5.5
- 63.5
- #15/22
- Basis
- Provisional lane · 0 vs 2 weighted rows
- Reading
- Not comparable
Math
Not comparable- GPT-4.1
- 28.1
- Unranked · 2 rankable rows
- GPT-5.5
- 69.6
- Unranked · 3 rankable rows
- Basis
- Provisional lane · 2 vs 2 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- GPT-4.1
- Not ranked
- GPT-5.5
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- GPT-4.1
- 50.2
- Unranked · 1 rankable row
- GPT-5.5
- 71.3
- #19/48
- Basis
- Provisional lane · 0 vs 2 weighted rows
- Reading
- Not comparable
| Category | GPT-4.1 | GPT-5.5 | Basis | Reading |
|---|---|---|---|---|
| Coding | 38.8Supported · #143/183 | 67.7Supported · #8/183 | Like-for-likeBenchAlign lane · 1 vs 9 public rows | GPT-5.5 leads |
| Knowledge | 40.7Supported · #135/181 | 73.3Supported · #7/181 | Like-for-likeBenchAlign lane · 2 vs 6 public rows | GPT-5.5 leads |
| Instruction following | 50.3#80/120 | 92.9#7/120 | Directional onlyProvisional lane · 0 vs 0 weighted rows | Directional only |
| Agentic | Not ranked | 63.9Supported · #15/151 | Not comparableBenchAlign lane · 1 vs 13 public rows | Not comparable |
| Reasoning | 67.2Unranked · 2 rankable rows | 63.5#15/22 | Not comparableProvisional lane · 0 vs 2 weighted rows | Not comparable |
| Math | 28.1Unranked · 2 rankable rows | 69.6Unranked · 3 rankable rows | Not comparableProvisional lane · 2 vs 2 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | 50.2Unranked · 1 rankable row | 71.3#19/48 | Not comparableProvisional lane · 0 vs 2 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.
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.
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.
FrontierMath v2 (Tiers 1-3)
Math
FrontierMath v2 (Tier 4)
Math
GPQA
Knowledge
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
- GPT-4.1
- $0.006
- Fits in one request
- GPT-5.5
- $0.02
- Fits in one request
GPT-4.1 has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- GPT-4.1
- $0.124
- Fits in one request
- GPT-5.5
- $0.34
- Fits in one request
GPT-4.1 has the lower modeled cost
Costs use the listed standard API rates.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- GPT-4.1
- $0.52
- Fits in one request
- Cached input priced at the published list-input rate
- GPT-5.5
- $0.5
- Fits in one request
GPT-5.5 has the lower modeled cost
GPT-4.1 has no published cached-input rate, so cached tokens use its listed input rate.
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationGPT-4.1GPT-5.5
Context window
Maximum documented context; output-token limits may be lower.
GPT-4.1
1M
GPT-5.5
API model ID
GPT-4.1
Not sourced
GPT-5.5
gpt-5.5
OpenAI pricingCached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
GPT-4.1
Not published
GPT-5.5
$0.5 per 1M cached input tokens
OpenAI pricingDocumented inputs
GPT-4.1
Not sourced
GPT-5.5
Not sourced
Documented outputs
GPT-4.1
Not sourced
GPT-5.5
Not sourced
Provider availability
GPT-4.1
Not sourced
GPT-5.5
Not sourced
Reasoning profile
GPT-4.1
Non-Reasoning
GPT-5.5
Reasoning
Weight access
GPT-4.1
Proprietary
GPT-5.5
Proprietary
License
GPT-4.1
Proprietary
GPT-5.5
Proprietary
Release date
GPT-4.1
2025-04-14
GPT-5.5
2026-04-23
If you already use one of these models
- Deployment change
- Both entries list OpenAI as the provider. Confirm endpoint, model ID, limits, and feature support before switching.
- Quality signal
- GPT-5.5 has the higher public score, 73.27 versus 43.81, and the 90% score intervals do not overlap.
- Workload cost
- Repository review: $0.124 vs $0.34. Cache-heavy agent loop: $0.52 vs $0.5.
- Context tradeoff
- Both models list 1M.
Run the same representative tasks against both endpoints before changing production traffic.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence41 rows
Agentic
Gert Labs
Shared sourceGPT-4.125.65%GPT-5.572.93%GPT-5.5 leads this result
Terminal-Bench 2.0
GPT-4.1—GPT-5.582%SourceNot directly comparable
CyberGym
GPT-4.1—GPT-5.581.8%SourceNot directly comparable
BrowseComp
GPT-4.1—GPT-5.584.4%SourceNot directly comparable
OSWorld-Verified
GPT-4.1—GPT-5.578.7%SourceNot directly comparable
MCP Atlas
GPT-4.1—GPT-5.575.3%SourceNot directly comparable
Toolathlon
GPT-4.1—GPT-5.555.6%SourceNot directly comparable
τ²-bench results
GPT-4.1—GPT-5.598%SourceNot directly comparable
ResearchClawBench
GPT-4.1—GPT-5.517.0%SourceNot directly comparable
OSWorld 2.0
GPT-4.1—GPT-5.513.0%SourceNot directly comparable
JobBench
GPT-4.1—GPT-5.542.7%SourceNot directly comparable
ExploitGym
GPT-4.1—GPT-5.513.4%SourceNot directly comparable
Terminal-Bench 2.1 (Vals)
GPT-4.1—GPT-5.576.4%SourceNot directly comparable
Coding
SWE-bench Verified
GPT-4.154.6%SourceGPT-5.5—Not directly comparable
SWE-bench Pro
GPT-4.1—GPT-5.558.6%SourceNot directly comparable
Terminal-Bench 2.0
GPT-4.1—GPT-5.582.0%SourceNot directly comparable
Vibe Code Bench
GPT-4.1—GPT-5.569.85%SourceNot directly comparable
React Native Evals
GPT-4.1—GPT-5.584.7%SourceNot directly comparable
cursorBench31
GPT-4.1—GPT-5.559.2%SourceNot directly comparable
cursorBench32
GPT-4.1—GPT-5.558.4%SourceNot directly comparable
FrontierCode 1.1 Main
GPT-4.1—GPT-5.543.0%SourceNot directly comparable
LiveCodeBench (Vals)
GPT-4.1—GPT-5.585.3%SourceNot directly comparable
SWE-bench (Vals)
GPT-4.1—GPT-5.582.6%SourceNot directly comparable
Reasoning
Knowledge
MMLU
GPT-4.190.2%SourceGPT-5.5—Not directly comparable
GPQA
GPT-4.166.3%SourceGPT-5.593.6%SourceGPT-5.5 leads this result
GPQA-D
GPT-4.1—GPT-5.593.6%SourceNot directly comparable
HLE
GPT-4.1—GPT-5.552.2%SourceNot directly comparable
HLE w/o tools
GPT-4.1—GPT-5.541.4%SourceNot directly comparable
GPQA Diamond (Vals)
GPT-4.1—GPT-5.593.2%SourceNot directly comparable
MMLU-Pro (Vals)
GPT-4.1—GPT-5.588.1%SourceNot directly comparable
Math
FrontierMath v2 (Tiers 1-3)
Shared sourceGPT-4.15.517%GPT-5.551.700%GPT-5.5 leads this result
FrontierMath v2 (Tier 4)
Shared sourceGPT-4.10.000%GPT-5.535.400%GPT-5.5 leads this result
FrontierMath (legacy)
GPT-4.1—GPT-5.551.7%SourceNot directly comparable
Multimodal
Instruction following
IFEval
GPT-4.187.4%SourceGPT-5.5—Not directly comparable
Frequently asked questions
Which is better, GPT-4.1 or GPT-5.5?
GPT-5.5 has the higher public score, 73.27 versus 43.81, and the 90% score intervals do not overlap. That is the clearest overall quality signal in the current public data.
Which is better for coding, GPT-4.1 or GPT-5.5?
GPT-5.5 leads the public coding lane, 67.7 to 38.8, with Supported evidence for both models and non-overlapping 90% intervals.
Which is better for agentic tasks, GPT-4.1 or GPT-5.5?
GPT-4.1 is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, GPT-4.1 or GPT-5.5?
For the stated presets, chat costs $0.006 on GPT-4.1 and $0.02 on GPT-5.5; repository review costs $0.124 and $0.34; the cache-heavy agent loop costs $0.52 and $0.5. GPT-4.1 has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, GPT-4.1 or GPT-5.5?
Both models list the same context window, 1M.
Related comparisons
Last updated September 4, 2026
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