Claude Opus 5.5 vs GPT-4.1 mini

Updated September 23, 2026. Rank says Claude Opus 5.5 is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

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.

Model A

Claude Opus 5.5

Anthropic

88.45/100

Estimated · Public rank #2

90% interval 76.9–100.0

Model B

GPT-4.1 mini

OpenAI

28.68/100

Estimated · Public rank #167

90% interval 22.1–35.3

Shared results: 0
Claude Opus 5.5 only: 47
GPT-4.1 mini only: 5
Like-for-like categories: 1 / 8

Estimated: Claude Opus 5.5 and GPT-4.1 miniHow 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.

Chat turn cost
1K fresh input + 500 output tokens
GPT-4.1 mini
GPT-4.1 mini 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-4.1 mini
GPT-4.1 mini has the lower estimated token cost for this stated workload. GPT-4.1 mini 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 mini
GPT-4.1 mini has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
GPT-4.1 mini is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
GPT-4.1 mini 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

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.

Pick the job you would use them for

The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.

87.1Claude Opus 5.524.9GPT-4.1 mini

Directional only · BenchAlign v5.6

Claude Opus 5.5 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.

Open the coding leaderboard Shortlist models for this job

Same basis rules as the category table below

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 v5.6 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 Opus 5.5: 90.0; Supported · #1/160
GPT-4.1 mini: 29.8; Supported · #128/160
Basis: BenchAlign v5.6 lane · 16 vs 2 public rows
Reading: Claude Opus 5.5 leads

Coding

Directional only

Claude Opus 5.5: 87.1; Supported · #1/135
GPT-4.1 mini: 24.9; Estimated · #111/135
Basis: BenchAlign v5.6 lane · 9 vs 1 public rows
Reading: Directional only

Agentic

Not comparable

Claude Opus 5.5: 88.8; Supported · #1/105
GPT-4.1 mini: Not ranked
Basis: BenchAlign v5.6 lane · 11 vs 0 public rows
Reading: Not comparable

Reasoning

Not comparable

Claude Opus 5.5: 78.5; #4/18
GPT-4.1 mini: 51.3; Unranked · 2 rankable rows
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Multimodal

Not comparable

Claude Opus 5.5: 88.8; #3/50
GPT-4.1 mini: 46.6; Unranked · 1 rankable row
Basis: Provisional lane · 1 vs 0 weighted rows
Reading: Not comparable

Multilingual

Not comparable

Claude Opus 5.5: Not ranked
GPT-4.1 mini: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Instruction following

Not comparable

Claude Opus 5.5: Not ranked
GPT-4.1 mini: 42.8; #94/124
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

Claude Opus 5.5: Not ranked
GPT-4.1 mini: 28.4; Unranked · 1 rankable row
Basis: Provisional lane · 0 vs 1 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for Claude Opus 5.5 and GPT-4.1 mini
Category	Claude Opus 5.5	GPT-4.1 mini	Basis	Reading
Knowledge	90.0Supported · #1/160	29.8Supported · #128/160	Like-for-likeBenchAlign v5.6 lane · 16 vs 2 public rows	Claude Opus 5.5 leads
Coding	87.1Supported · #1/135	24.9Estimated · #111/135	Directional onlyBenchAlign v5.6 lane · 9 vs 1 public rows	Directional only
Agentic	88.8Supported · #1/105	Not ranked	Not comparableBenchAlign v5.6 lane · 11 vs 0 public rows	Not comparable
Reasoning	78.5#4/18	51.3Unranked · 2 rankable rows	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Multimodal	88.8#3/50	46.6Unranked · 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	42.8#94/124	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Math	Not ranked	28.4Unranked · 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 v5.6) 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 Opus 5.5: $0.014; Fits in one request
GPT-4.1 mini: $0.0012; Fits in one request

GPT-4.1 mini has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

Claude Opus 5.5: $0.26; Fits in one request
GPT-4.1 mini: $0.0248; Fits in one request

GPT-4.1 mini 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 Opus 5.5: $0.32; Fits in one request
GPT-4.1 mini: $0.104; Fits in one request; Cached input priced at the published list-input rate

GPT-4.1 mini has the lower modeled cost

GPT-4.1 mini 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 Opus 5.5GPT-4.1 mini

Context window

Maximum documented context; output-token limits may be lower.

Claude Opus 5.5

Anthropic Claude Opus 5.5 model documentation

GPT-4.1 mini

API model ID

Claude Opus 5.5

claude-opus-5-5

Anthropic Claude Opus 5.5 model documentation

GPT-4.1 mini

Not sourced

Cached-input rate

A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.

Claude Opus 5.5

$0.2 per 1M cached input tokens

Claude Opus 5.5 model documentation

GPT-4.1 mini

Not published

Documented inputs

Claude Opus 5.5

text, image

Anthropic Claude Opus 5.5 model documentation

GPT-4.1 mini

Not sourced

Documented outputs

Claude Opus 5.5

text

Anthropic Claude Opus 5.5 model documentation

GPT-4.1 mini

Not sourced

Provider availability

Claude Opus 5.5

Generally Available · Claude API, Amazon Bedrock, Google Cloud, Microsoft Foundry, Claude Platform on AWS

Anthropic Claude Opus 5.5 model documentation

GPT-4.1 mini

Not sourced

Reasoning profile

Claude Opus 5.5

Reasoning

GPT-4.1 mini

Non-Reasoning

Weight access

Claude Opus 5.5

Proprietary

GPT-4.1 mini

Proprietary

License

Claude Opus 5.5

Proprietary

GPT-4.1 mini

Proprietary

Release date

Claude Opus 5.5

2026-09-22

GPT-4.1 mini

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: $0.26 vs $0.0248. Cache-heavy agent loop: $0.32 vs $0.104.
Context tradeoff: Both models list 1M.

Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, Claude Opus 5.5 or GPT-4.1 mini?

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 Opus 5.5 or GPT-4.1 mini?

Claude Opus 5.5 scores higher for coding on the public lane, 87.1 to 24.9. GPT-4.1 mini 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 Opus 5.5 or GPT-4.1 mini?

GPT-4.1 mini is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, Claude Opus 5.5 or GPT-4.1 mini?

For the stated presets, chat costs $0.014 on Claude Opus 5.5 and $0.0012 on GPT-4.1 mini; repository review costs $0.26 and $0.0248; the cache-heavy agent loop costs $0.32 and $0.104. GPT-4.1 mini has no published cached-input rate, so cached tokens use its listed input rate.

Which has the larger context window, Claude Opus 5.5 or GPT-4.1 mini?

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 evidence52 rows

Agentic

Terminal-Bench 4.0
Claude Opus 5.566.40%
Source
GPT-4.1 mini—
Not directly comparable
Terminal-Bench-Science 0.1
Claude Opus 5.558.7%
Source
GPT-4.1 mini—
Not directly comparable
AutomationBench
Claude Opus 5.540.0%
Source
GPT-4.1 mini—
Not directly comparable
HLE w/ tools
Claude Opus 5.567.7%
Source
GPT-4.1 mini—
Not directly comparable
OSWorld 2.0
Claude Opus 5.548.7%
Source
GPT-4.1 mini—
Not directly comparable
LAB all-pass (Harvey held-out)
Claude Opus 5.58.3%
Source
GPT-4.1 mini—
Not directly comparable
LAB criterion-pass (Harvey held-out)
Claude Opus 5.591.2%
Source
GPT-4.1 mini—
Not directly comparable
Toolathlon-Verified
Claude Opus 5.577.8%
Source
GPT-4.1 mini—
Not directly comparable
Toolathlon Verified Pass@3
Claude Opus 5.582.4%
Source
GPT-4.1 mini—
Not directly comparable
Toolathlon Verified Pass³
Claude Opus 5.572.2%
Source
GPT-4.1 mini—
Not directly comparable
Toolathlon Verified avg. turns
Claude Opus 5.526.9 turns
Source
GPT-4.1 mini—
Not directly comparable

Coding

FrontierCode 1.1 Main
Claude Opus 5.554.4%
Source
GPT-4.1 mini—
Not directly comparable
cursorBench40
Claude Opus 5.557.8%
Source
GPT-4.1 mini—
Not directly comparable
SWE-bench Pro
Claude Opus 5.589.9%
Source
GPT-4.1 mini—
Not directly comparable
SWE Multilingual
Claude Opus 5.593.9%
Source
GPT-4.1 mini—
Not directly comparable
SWE Multimodal
Claude Opus 5.561.4%
Source
GPT-4.1 mini—
Not directly comparable
DeepSWE
Claude Opus 5.574.2%
Source
GPT-4.1 mini—
Not directly comparable
FrontierCode 1.1 Extended
Claude Opus 5.563.6%
Source
GPT-4.1 mini—
Not directly comparable
FrontierSWE v2
Claude Opus 5.562.3%
Source
GPT-4.1 mini—
Not directly comparable
ProgramBench
Claude Opus 5.591.2%
Source
GPT-4.1 mini—
Not directly comparable
SWE-bench Verified
Claude Opus 5.5—
GPT-4.1 mini23.6%
Source
Not directly comparable

Multimodal

Chartography (tools)
Claude Opus 5.589.0%
Source
GPT-4.1 mini—
Not directly comparable
Chartography (no tools)
Claude Opus 5.564.4%
Source
GPT-4.1 mini—
Not directly comparable
BenchCAD Vision2Code (no tools)
Claude Opus 5.50.730
Source
GPT-4.1 mini—
Not directly comparable
BenchCAD Vision2Code (tools)
Claude Opus 5.50.962
Source
GPT-4.1 mini—
Not directly comparable
Biomedical image analysis
Claude Opus 5.571.4%
Source
GPT-4.1 mini—
Not directly comparable
OfficeQA
Claude Opus 5.578.9%
Source
GPT-4.1 mini—
Not directly comparable
OfficeQA Pro
Claude Opus 5.567.7%
Source
GPT-4.1 mini—
Not directly comparable

Knowledge

HLE w/o tools
Claude Opus 5.564.4%
Source
GPT-4.1 mini—
Not directly comparable
HealthBench (raw)
Claude Opus 5.568.1%
Source
GPT-4.1 mini—
Not directly comparable
HealthBench (length-adjusted)
Claude Opus 5.560.6%
Source
GPT-4.1 mini—
Not directly comparable
HealthBench Professional
Claude Opus 5.565.6%
Source
GPT-4.1 mini—
Not directly comparable
HealthBench Professional (raw)
Claude Opus 5.577.1%
Source
GPT-4.1 mini—
Not directly comparable
BioMysteryBench (human-solvable)
Claude Opus 5.589.3%
Source
GPT-4.1 mini—
Not directly comparable
BioMysteryBench (human-difficult)
Claude Opus 5.550.0%
Source
GPT-4.1 mini—
Not directly comparable
SpatialBench Verified
Claude Opus 5.572.0%
Source
GPT-4.1 mini—
Not directly comparable
SingleCellBench
Claude Opus 5.561.2%
Source
GPT-4.1 mini—
Not directly comparable
Morphology-to-molecule matching
Claude Opus 5.534.0%
Source
GPT-4.1 mini—
Not directly comparable
Medicinal chemistry
Claude Opus 5.563.5%
Source
GPT-4.1 mini—
Not directly comparable
Protein Design
Claude Opus 5.560.2%
Source
GPT-4.1 mini—
Not directly comparable
Protein Design library ranking
Claude Opus 5.556.0%
Source
GPT-4.1 mini—
Not directly comparable
De novo protein-binder design
Claude Opus 5.582.6%
Source
GPT-4.1 mini—
Not directly comparable
Protocols (troubleshooting)
Claude Opus 5.573.7%
Source
GPT-4.1 mini—
Not directly comparable
Protocols (understanding)
Claude Opus 5.569.0%
Source
GPT-4.1 mini—
Not directly comparable
MMLU
Claude Opus 5.5—
GPT-4.1 mini87.5%
Source
Not directly comparable
GPQA
Claude Opus 5.5—
GPT-4.1 mini64.2%
Source
Not directly comparable

Multilingual

GMMLU
Claude Opus 5.594.3%
Source
GPT-4.1 mini—
Not directly comparable
MILU
Claude Opus 5.593.1%
Source
GPT-4.1 mini—
Not directly comparable

Instruction following

IFEval
Claude Opus 5.5—
GPT-4.1 mini88.5%
Source
Not directly comparable

Math

ArXivMath Aug. 2026 (no tools)
Claude Opus 5.591.2%
Source
GPT-4.1 mini—
Not directly comparable
ArXivMath Aug. 2026 (tools)
Claude Opus 5.596.9%
Source
GPT-4.1 mini—
Not directly comparable
FrontierMath v2 (Tiers 1-3)
Claude Opus 5.5—
GPT-4.1 mini4.483%
Source
Not directly comparable

52 public results · 0 shared

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Last updated September 23, 2026

Claude Opus 5.5 vs GPT-4.1 mini

Decision reading

Which one for your work

Chat turn cost

Cache-heavy agent loop cost

Repository review cost

Coding work

Agentic work

Long documents

Which one for a specific job

What is actually comparable

Shape of the matched evidence

Category results, on a stated basis

Knowledge

Coding

Agentic

Reasoning

Multimodal

Multilingual

Instruction following

Math

What each workload costs

Chat turn

Repository review

Cache-heavy agent loop

Specification differences

Context window

API model ID

Cached-input rate

Documented inputs

Documented outputs

Provider availability

Reasoning profile

Weight access

License

Release date

If you already use one of these models

Questions

Which is better, Claude Opus 5.5 or GPT-4.1 mini?

Which is better for coding, Claude Opus 5.5 or GPT-4.1 mini?

Which is better for agentic tasks, Claude Opus 5.5 or GPT-4.1 mini?

Which costs less, Claude Opus 5.5 or GPT-4.1 mini?

Which has the larger context window, Claude Opus 5.5 or GPT-4.1 mini?

Benchmark evidence

Agentic

Coding

Multimodal

Knowledge

Multilingual

Instruction following

Math

Related comparisons

Watch Claude Opus 5.5 vs GPT-4.1 mini