Models · /models/deepseek-chat
DeepSeek Chat
DeepSeek · Open weights · budget · registry tag 2026 open generalist
textcode6 aliases6 official receipts
Build / data stamp
Read this before trusting a headline.
Data snapshot May 1, 2026Registry verification passed9 providers · 826 tracked modelsPage refreshed May 7, 2026
Model pages expose the current registry snapshot and page stamp so stale deployments are visible without reading the code.
Score passport by benchmark
Each row keeps the benchmark receipt, source family, raw metric, and percentile inside its exact comparable group.
Thin verified coverageThis model currently reads as thin verified coverage across the resolved evidence surface.
Intelligence Index
AA · Chat / text · Composite
It tests whether the model is actually useful in normal conversational turns, not just on narrow correctness tasks.
71.4% percentile inside its comparable group
32Raw benchmark value
Text Arena
AR · Chat / text · Human
It tests whether the model is actually useful in normal conversational turns, not just on narrow correctness tasks.
85.1% percentile inside its comparable group
1,423Raw benchmark value
Code Arena
AR · Coding · Human
It tells you whether the model can generate, repair, and reason over code under evaluator pressure rather than marketing examples.
35% percentile inside its comparable group
1,332Raw benchmark value
WebDev Arena
AR · Coding · Human
It tells you whether the model can generate, repair, and reason over code under evaluator pressure rather than marketing examples.
35% percentile inside its comparable group
1,332Raw benchmark value
Reasoning
LB · Reasoning / math / science · Objective
It is one of the cleaner reads on deliberate reasoning strength rather than style or popularity.
19.4% percentile inside its comparable group
46.4%Raw benchmark value
Instruction following
LB · Chat / text · Objective
It tests whether the model is actually useful in normal conversational turns, not just on narrow correctness tasks.
16.1% percentile inside its comparable group
67.8%Raw benchmark value
Language
LB · Chat / text · Objective
It tests whether the model is actually useful in normal conversational turns, not just on narrow correctness tasks.
12.9% percentile inside its comparable group
32.1%Raw benchmark value
Coding
LB · Coding · Objective
It tells you whether the model can generate, repair, and reason over code under evaluator pressure rather than marketing examples.
45.2% percentile inside its comparable group
46.2%Raw benchmark value
Coding completion
LB · Coding · Objective
It tells you whether the model can generate, repair, and reason over code under evaluator pressure rather than marketing examples.
54.8% percentile inside its comparable group
50%Raw benchmark value
Coding generation
LB · Coding · Objective
It tells you whether the model can generate, repair, and reason over code under evaluator pressure rather than marketing examples.
29% percentile inside its comparable group
42.3%Raw benchmark value