DeepSeek V3 deepseek-v3

deepseek-ai · deepseek flagship

#77 overall #5 coding #20 agentic

params
671B (A37B)
arch
moe
context
160k
license
mit open weights
released
Dec 2024
train compute
3.3e+24 FLOP
downloads/30d
1.4M

# architecture

attn shared MHA ×128 router top-8 of 256 +1 shared expert ×256 expert out ×61 layers ctx 163,840 671B pool · A37B/token
layers
61
d_model
7168
heads
128q / 128kv
head dim
56
experts
top-8 of 256 + 1 shared
vocab
129280
family
deepseek_v3

signal path of one layer, generated from the registry's structured fields — dimension lines quote the model's real numbers; an MoE trace forks at the router, a dense trace runs straight through. Geometry fields come from the repo's config.json.

# why ranked

Overall: #77 — score 30.7 ● high — all signals present

signalweightinput (0–100)
aa_intelligence 0.70 31.3
bench_composite 0.30 29.3

benchmark panel evidence:

complete panelscore (0–100)
aa-coding-v138.1
aa-math-v120.9
aider-polyglot-v138.5
swe-bench-verified-v119.6

Coding: #5 — score 29.0 ● high — all signals present

signalweightinput (0–100)
aa_coding 0.30 27.9
aider_polyglot 0.30 54.5
swe_bench_verified 0.40 10.5

Agentic: #20 — score 10.5 ● low — a single signal; treat with caution

signalweightinput (0–100)
swe_bench_verified 0.60 10.5
swe_rebench 0.40 —

missing signals are dropped and the remaining weights renormalized — never imputed.

full methodology

# trend

OGM score · last 106 days
OGM score over 106 days
overall rank (up is better)
Overall rank over 106 days

methodology changed during this history window; score movement across that boundary is not model movement. See methodology v6.

# benchmarks

benchmarkscoresourcedate
AA Coding Index 23.0 Artificial Analysis —
AA Intelligence Index 9.7 Artificial Analysis —
AA Math Index 41.0 Artificial Analysis —
Aider Polyglot 55.1 Aider Mar 2025
Aider-polyglot 0.5 / 1 LLM Stats —
Aider-polyglot-edit 0.8 / 1 LLM Stats —
AIME 2024 0.6 / 1 LLM Stats —
C-eval 0.9 / 1 LLM Stats —
Cluewsc 0.9 / 1 LLM Stats —
Cnmo-2024 0.4 / 1 LLM Stats —
SWE-bench Verified 42.0 SWE-bench Aug 2025

# where to run

providerquantctx$/M in$/M out$/M cacheprice srctpsuptime✓
SiliconFlow — ——— siliconflow — — ✓
Hyperbolic 32k $0.20$0.20— via litellm — —
Inferenceapis 1024k $0.19$0.38— via litellm — —
DeepSeek 128k $0.28$0.42— via litellm — —
StreamLakeunknown 125k $0.26$1.03— via openrouter — 100.0%
DeepInfrafp4 160k $0.32$0.89— deepinfra — 100.0% ✓
DeepInfrafp4 160k $0.32$0.89— deepinfra — 96.7% ✓
Novita 125k $0.40$1.30$0.40 via requesty — —
Nebius 125k $0.50$1.50— via litellm — —
Amazon Bedrock 125k $0.58$1.68— via litellm — —
Together 128k $1.25$1.25— via modelsdev — —
Replicate 64k $1.45$1.45— via litellm — —
Azure 125k $1.14$4.56— via litellm — —

sorted by blended price ((3·input + output) / 4 per 1M) · ✓ = the provider's own catalog confirms the offer · "via …" prices are what the aggregator routing the offer charges, not the provider's own list price

# variants

DeepSeek R1 DeepSeek V3.1 DeepSeek V3.2

source aliases
aa
deepseek-v3, deepseek-v3-0324
aider
DeepSeek Chat V3 (prev), DeepSeek V3 (0324)
arena
DeepSeek-V3
epoch
DeepSeek-V3 (Mar 2025)
litellm
deepseek/deepseek-chat
llmstats
deepseek-v3-0324
openrouter
deepseek/deepseek-chat
swebench
DeepSeek-V3-0324