Compare · pick a model
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Current frameBest for this use caseCoding copilot · All public sources
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GPT-5 leads this compare set for coding copilot.
Read this page as a workspace for choosing between top models, not as a universal crown. The current frame is Coding copilot under best for this use case.
Visible tradeoffsThe current evidence supports a shortlist, not a single winner.
Leader for this use caseGPT-5
Closest objectionNo clear runner-up yet
Shared tests18 shared benchmarks · 0 close calls
SourcesAll public sources
- Why the leader is ahead
- Document understanding · Vision understanding
- Where the evidence is thin
- 0 close-call benchmarks and 22 missing missing leader rows keep this from reading as settled.
- What to do next
- Inspect decisive benchmarks first, then open the disagreement page or head-to-head pages if the top line still feels too narrow.
18 of 40 benchmarks
| Text Arena AR · rating Text · Chat / text | 1,386n/a | n/a |
| Code Arena AR · rating Code · Coding | 1,39462.5% | n/a |
| Vision Arena AR · rating Vision · Vision understanding | 1,21168.3% | n/a |
| WebDev Arena AR · rating Code · Coding | 1,39462.5% | n/a |
| Search Arena AR · rating Search · Search / tool use | 1,134n/a | n/a |
| Intelligence Index AA · index Text · Chat / text | 2252.4% | n/a |
| Time to first token AA · s Text · Chat / text | 79.00s3.8% | n/a |
| Long Context Reasoning AA · % Document · Long context | 75.6%20% | n/a |
| Terminal-Bench 2.0 TERMINAL-BENCH · % Code · Coding | 49.6%70% | n/a |
| EnigmaEval SL · % Text · Reasoning / math / science | 64%60% | n/a |
| VISTA SL · % Vision · Vision understanding | 79%71.4% | n/a |
| TutorBench SL · % Text · Reasoning / math / science | 55.3%60% | n/a |
| VTB SL · % Vision · Vision understanding | 17%45.5% | n/a |
| PRBench Legal SL · % Text · Professional reasoning | 49%66.7% | n/a |
| MASK SL · % Text · Safety | 79.3%46.2% | n/a |
| MultiNRC SL · % Text · Reasoning / math / science | 52.1%40% | n/a |
| Multimodal mix OC · % Document · Document understanding | 75.4%71.4% | n/a |
| Retrieval MTEB · ndcg Embedding · Embeddings / retrieval | 58.8 ndcg70% | n/a |
What is doing the visible work
- No decisive benchmark
- The shared tests are too split or too sparse for a clean separator.
What changes the winner
- GPT-5
- 22 visible benchmark gaps still leave room for the result to move.
Where evidence is missing
GPT-5
Missing visible evidence on this compare surface
22
How this weighting reads the field
The current decision mode is grounded in the Coding copilot preset. This keeps the compare page connected to a visible use case instead of an unspoken “overall winner” claim.
score = 1.45 × coding + 1.05 × reasoning math science + 0.80 × long contextcoverage floor = 55% · recency window = 120 days
GPT-5
OpenAI
54.1%
Reading guide
How to read this workspace
Who wins most oftenBenchmarks with one clear percentile leader.Missing evidenceBenchmarks where a top model has no visible score.Decision readA public claim tied to an explicit use case and source filter.