Agentic
Not comparable- Claude Sonnet 5
- 81.9
- GPT-4o mini Audio
- Not measured
- Weighted basis
- 3 vs 0 rows
- Reading
- Not comparable
Model comparison
Claude Sonnet 5 vs GPT-4o mini Audio
Updated August 4, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
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 based on different benchmark sets are marked directional and do not name a winner.
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.
Long documents
Prompts that approach the documented context limit
Claude Sonnet 5
Claude Sonnet 5 has the larger documented context window.
Confidence: documented
Chat turn cost
1K fresh input + 500 output tokens
GPT-4o mini Audio
GPT-4o mini Audio has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Show secondary and unsupported calls
Repository review cost
50K fresh input + 3K output tokens
GPT-4o mini Audio
GPT-4o mini Audio 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
No shared weighted benchmark basis supports a winner.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
No shared weighted benchmark basis supports a winner.
Confidence: limited
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Not enough matched evidence
The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. GPT-4o mini Audio does not fit this workload in one request. GPT-4o mini Audio has no published cached-input rate, so cached tokens use its listed input rate.
Confidence: rate-fallback
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
15 Claude Sonnet 5 only
- Shared results
- 0
- Claude Sonnet 5 only
- 15
- GPT-4o mini Audio only
- 0
- Like-for-like categories
- 0 / 8
Category results, on a stated basis
Each row states whether both averages use the same weighted benchmark set. Directional and not-comparable rows remain visible, but they never receive a winner in this template.
Coding
Not comparable- Claude Sonnet 5
- 76.7
- GPT-4o mini Audio
- Not measured
- Weighted basis
- 2 vs 0 rows
- Reading
- Not comparable
Reasoning
Not comparable- Claude Sonnet 5
- Not measured
- GPT-4o mini Audio
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Knowledge
Not comparable- Claude Sonnet 5
- 57.4
- GPT-4o mini Audio
- Not measured
- Weighted basis
- 1 vs 0 rows
- Reading
- Not comparable
Math
Not comparable- Claude Sonnet 5
- Not measured
- GPT-4o mini Audio
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multilingual
Not comparable- Claude Sonnet 5
- Not measured
- GPT-4o mini Audio
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multimodal
Not comparable- Claude Sonnet 5
- 88.3
- GPT-4o mini Audio
- Not measured
- Weighted basis
- 1 vs 0 rows
- Reading
- Not comparable
Instruction following
Not comparable- Claude Sonnet 5
- Not measured
- GPT-4o mini Audio
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
| Category | Claude Sonnet 5 | GPT-4o mini Audio | Weighted basis | Reading |
|---|---|---|---|---|
| Agentic | 81.9 | Not measured | Not comparable3 vs 0 rows | Not comparable |
| Coding | 76.7 | Not measured | Not comparable2 vs 0 rows | Not comparable |
| Reasoning | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Knowledge | 57.4 | Not measured | Not comparable1 vs 0 rows | Not comparable |
| Math | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Multilingual | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Multimodal | 88.3 | Not measured | Not comparable1 vs 0 rows | Not comparable |
| Instruction following | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
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.
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-4o mini Audio
- $0.00045
- Fits in one request
GPT-4o mini Audio 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-4o mini Audio
- $0.0093
- Fits in one request
GPT-4o mini Audio 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-4o mini Audio
- $0.039
- Does not fit in one request
- Cached input priced at the published list-input rate
GPT-4o mini Audio does not fit this workload in one request. GPT-4o mini Audio 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.
SpecificationClaude Sonnet 5GPT-4o mini Audio
Context window
Maximum documented context; output-token limits may be lower.
Claude Sonnet 5
GPT-4o mini Audio
API model ID
Claude Sonnet 5
claude-sonnet-5
Anthropic model overviewGPT-4o mini Audio
gpt-4o-mini-audio-preview
OpenAI model documentationCached-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-4o mini Audio
Not published
OpenAI model documentationDocumented inputs
Claude Sonnet 5
text, image
Anthropic model overviewGPT-4o mini Audio
Not sourced
Documented outputs
Claude Sonnet 5
GPT-4o mini Audio
Not sourced
Provider availability
Claude Sonnet 5
Generally Available · Claude API
Anthropic model overviewGPT-4o mini Audio
Not sourced
Reasoning profile
Claude Sonnet 5
Reasoning
GPT-4o mini Audio
Non-Reasoning
Weight access
Claude Sonnet 5
Proprietary
GPT-4o mini Audio
Proprietary
License
Claude Sonnet 5
Proprietary
GPT-4o mini Audio
Proprietary
Release date
Claude Sonnet 5
2026-06-30
GPT-4o mini Audio
Not sourced
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 $0.0093. Cache-heavy agent loop: $0.18 vs $0.039.
- Context tradeoff
- Claude Sonnet 5 has the larger documented window (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 evidence15 rows
Agentic
Terminal-Bench 2.0
Claude Sonnet 580.4%SourceGPT-4o mini Audio—Not directly comparable
BrowseComp
Claude Sonnet 584.7%SourceGPT-4o mini Audio—Not directly comparable
HLE w/ tools
Claude Sonnet 557.4%SourceGPT-4o mini Audio—Not directly comparable
OSWorld-Verified
Claude Sonnet 581.2%SourceGPT-4o mini Audio—Not directly comparable
Coding
SWE-bench Verified
Claude Sonnet 585.2%SourceGPT-4o mini Audio—Not directly comparable
SWE-bench Pro
Claude Sonnet 563.2%SourceGPT-4o mini Audio—Not directly comparable
SWE Multilingual
Claude Sonnet 578.3%SourceGPT-4o mini Audio—Not directly comparable
SWE Multimodal
Claude Sonnet 528.1%SourceGPT-4o mini Audio—Not directly comparable
Terminal-Bench 2.0
Claude Sonnet 580.4%SourceGPT-4o mini Audio—Not directly comparable
FrontierCode 1.1 Main
Claude Sonnet 542.7%SourceGPT-4o mini Audio—Not directly comparable
cursorBench32
Claude Sonnet 561.5%SourceGPT-4o mini Audio—Not directly comparable
Knowledge
Multimodal
Frequently asked questions
Which is better, Claude Sonnet 5 or GPT-4o mini Audio?
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-4o mini Audio?
The published evidence does not provide a shared weighted coding basis for both models, so BenchLM does not name a coding winner.
Which is better for agentic tasks, Claude Sonnet 5 or GPT-4o mini Audio?
The published evidence does not provide a shared weighted agentic tasks basis for both models, so BenchLM does not name a agentic tasks winner.
Which costs less, Claude Sonnet 5 or GPT-4o mini Audio?
For the stated presets, chat costs $0.007 on Claude Sonnet 5 and $0.00045 on GPT-4o mini Audio; repository review costs $0.13 and $0.0093; the cache-heavy agent loop costs $0.18 and $0.039. GPT-4o mini Audio does not fit this workload in one request. GPT-4o mini Audio 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-4o mini Audio?
Claude Sonnet 5 has the larger documented context window: 1M, compared with 128K.
Related comparisons
Last updated August 4, 2026
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