Coding
Directional only- GPT-5 (medium)
- 50.6
- Estimated · #69/183
- GPT-5 mini
- 41.4
- Estimated · #133/183
- Basis
- BenchAlign lane · 0 vs 1 public rows
- Reading
- Directional only
GPT-5 (medium) vs GPT-5 mini
Updated September 4, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload. This is a same-family comparison, so migration details appear when the source data supports them.
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.
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.
No workload recommendation clears the current evidence threshold.
Use the matched evidence, workload costs, and sourced specifications below instead of treating a point score as a universal answer.
Show secondary and unsupported calls
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
GPT-5 (medium) and GPT-5 mini are 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-5 (medium) 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
Chat turn cost
1K fresh input + 500 output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
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-5 (medium) does not fit this workload in one request. GPT-5 mini does not fit this workload in one request. GPT-5 mini has no published cached-input rate, so cached tokens use its listed input rate. GPT-5 (medium) has no comparable published API token rate.
Confidence: rate-fallback
Repository review cost
50K fresh input + 3K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
1 GPT-5 mini only
- Shared results
- 0
- GPT-5 (medium) only
- 0
- GPT-5 mini only
- 1
- Like-for-like categories
- 0 / 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.
Agentic
Not comparable- GPT-5 (medium)
- Not ranked
- GPT-5 mini
- 42.3
- Estimated · #114/151
- Basis
- BenchAlign lane · 0 vs 0 public rows
- Reading
- Not comparable
Reasoning
Not comparable- GPT-5 (medium)
- 75.9
- Unranked · 2 rankable rows
- GPT-5 mini
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- GPT-5 (medium)
- 52.7
- Estimated · #72/181
- GPT-5 mini
- Not ranked
- Basis
- BenchAlign lane · 0 vs 0 public rows
- Reading
- Not comparable
Math
Not comparable- GPT-5 (medium)
- Not ranked
- GPT-5 mini
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- GPT-5 (medium)
- Not ranked
- GPT-5 mini
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- GPT-5 (medium)
- 69.2
- Unranked · 1 rankable row
- GPT-5 mini
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- GPT-5 (medium)
- 86.1
- #37/120
- GPT-5 mini
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | GPT-5 (medium) | GPT-5 mini | Basis | Reading |
|---|---|---|---|---|
| Coding | 50.6Estimated · #69/183 | 41.4Estimated · #133/183 | Directional onlyBenchAlign lane · 0 vs 1 public rows | Directional only |
| Agentic | Not ranked | 42.3Estimated · #114/151 | Not comparableBenchAlign lane · 0 vs 0 public rows | Not comparable |
| Reasoning | 75.9Unranked · 2 rankable rows | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Knowledge | 52.7Estimated · #72/181 | Not ranked | Not comparableBenchAlign lane · 0 vs 0 public rows | Not comparable |
| Math | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | 69.2Unranked · 1 rankable row | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | 86.1#37/120 | Not ranked | Not comparableProvisional lane · 0 vs 0 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.
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
- GPT-5 (medium)
- API rate not published
- Fits in one request
- GPT-5 mini
- $0.00125
- Fits in one request
GPT-5 (medium) has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- GPT-5 (medium)
- API rate not published
- Fits in one request
- GPT-5 mini
- $0.0185
- Fits in one request
GPT-5 (medium) has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- GPT-5 (medium)
- API rate not published
- Does not fit in one request
- Cached-input rate unavailable
- GPT-5 mini
- $0.075
- Does not fit in one request
- Cached input priced at the published list-input rate
GPT-5 (medium) does not fit this workload in one request. GPT-5 mini does not fit this workload in one request. GPT-5 mini has no published cached-input rate, so cached tokens use its listed input rate. GPT-5 (medium) has no comparable published API token rate.
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationGPT-5 (medium)GPT-5 mini
Context window
Maximum documented context; output-token limits may be lower.
GPT-5 (medium)
128K
GPT-5 mini
128K
API model ID
GPT-5 (medium)
Not sourced
GPT-5 mini
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
GPT-5 (medium)
No comparable hosted API rate
GPT-5 mini
Not published
Documented inputs
GPT-5 (medium)
Not sourced
GPT-5 mini
Not sourced
Documented outputs
GPT-5 (medium)
Not sourced
GPT-5 mini
Not sourced
Provider availability
GPT-5 (medium)
Not sourced
GPT-5 mini
Not sourced
Reasoning profile
GPT-5 (medium)
Reasoning
GPT-5 mini
Reasoning
Weight access
GPT-5 (medium)
Proprietary
GPT-5 mini
Proprietary
License
GPT-5 (medium)
Proprietary
GPT-5 mini
Proprietary
Release date
GPT-5 (medium)
2025-08-07
GPT-5 mini
2025-08-07
If you are choosing between sibling variants
- Deployment change
- Both entries list OpenAI as the provider. Confirm endpoint, model ID, limits, and feature support before switching.
- Quality signal
- The public evidence has no benchmark result shared by both models, so it does not support a quality verdict.
- Workload cost
- A complete comparable API-rate estimate is not available for both models.
- Context tradeoff
- Both models list 128K.
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 evidence1 rows
Coding
Vibe Code Bench
GPT-5 (medium)—GPT-5 mini14.17%SourceNot directly comparable
Frequently asked questions
Which is better, GPT-5 (medium) or GPT-5 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, GPT-5 (medium) or GPT-5 mini?
GPT-5 (medium) scores higher for coding on the public lane, 50.6 to 41.4. GPT-5 (medium) and GPT-5 mini are 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, GPT-5 (medium) or GPT-5 mini?
GPT-5 (medium) is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, GPT-5 (medium) or GPT-5 mini?
Both models do not have comparable published API token rates, so this page does not name a universal price winner.
Which has the larger context window, GPT-5 (medium) or GPT-5 mini?
Both models list the same context window, 128K.
Related comparisons
Last updated September 4, 2026
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