模型排行

排名	變化	模型	得分	置信區間	投票	價格 $/M
1	—	kimi-k3-max開源Moonshot AI	1,485	±5	20,987	—
2	—	glm-5.3-max開源Z.ai	1,483	±6	10,960	—
3	—	glm-5.3-flash開源Z.ai	1,475	±7	10,038	$0.24
4	—	glm-5.2-max開源Z.ai	1,472	±5	36,798	—
5	—	mimo-v2.5-pro開源Xiaomi	1,467	±4	60,919	—
6	—	glm-5.1開源Z.ai	1,466	±4	48,901	—
7	▲ 1	deepseek-v4-pro-high-20260813開源DeepSeek · 另有 1 個檔位	1,463	±7	9,008	—
8	▼ 1	kimi-k2.6開源Moonshot AI	1,460	±5	37,502	—
9	▲ 1	glm-5開源Z.ai	1,458	±4	27,605	—
10	▼ 1	deepseek-v4-pro開源DeepSeek	1,457	±4	54,130	—
11	—	hy3開源Tencent	1,456	±7	8,047	—
12	—	gemma-4-31b開源Google	1,451	±8	5,894	$0.000
13	—	kimi-k2.5-thinking開源Moonshot AI	1,450	±3	70,513	—
14	▲ 2	qwen3.5-397b-a17b開源Alibaba	1,442	±3	77,007	—
15	—	glm-4.7開源Z.ai	1,442	±6	11,893	—
16	▼ 2	minimax-m3開源MiniMax	1,441	±4	48,540	$0.52
17	—	inkling開源Thinking Machines	1,440	±5	25,922	$1.8
18	—	deepseek-v4-flash-high-preview開源DeepSeek	1,438	±4	48,597	—
19	—	gemma-4-26b-a4b開源Google	1,438	±8	5,804	—
20	—	qwen3.8-27b開源Alibaba	1,437	±6	10,697	$1.1
21	—	deepseek-v4-flash開源DeepSeek	1,436	±4	48,887	—
22	—	mimo-v2.5開源Xiaomi	1,434	±4	44,466	—
23	—	kimi-k2.5-instant開源Moonshot AI	1,431	±7	7,987	—
24	—	kimi-k2-thinking-turbo開源Moonshot AI	1,430	±3	61,088	—
25	—	muse-glimmer開源Meta	1,427	±10	3,666	$0.52
26	—	mistral-medium-3.5開源Mistral	1,426	±7	10,996	$3.0
27	—	nvidia-nemotron-3-ultra-550b-a55b-nvfp4開源NVIDIA	1,426	±7	10,693	—
28	▲ 1	deepseek-v3.2-exp-thinking開源DeepSeek · 另有 1 個檔位	1,425	±7	8,905	—
29	▼ 1	deepseek-v3.2開源DeepSeek · 另有 1 個檔位	1,425	±4	46,458	—
30	—	glm-4.6開源Z.ai	1,425	±4	35,061	—
31	—	qwen3-235b-a22b-instruct-2507開源Alibaba	1,423	±3	95,670	—
32	—	deepseek-r1-0528開源DeepSeek · 另有 1 個檔位	1,421	±6	18,091	—
33	—	kimi-k2-0905-preview開源Moonshot AI · 另有 1 個檔位	1,418	±7	11,568	—
34	—	deepseek-v3.1-terminus-thinking開源DeepSeek · 另有 1 個檔位	1,417	±10	3,369	—
35	—	deepseek-v3.1開源DeepSeek · 另有 1 個檔位	1,417	±6	14,607	—
36	—	qwen3.5-122b-a10b開源Alibaba	1,417	±4	28,359	—
37	—	minimax-m2.7開源MiniMax	1,415	±4	69,169	—
38	—	qwen3-vl-235b-a22b-instruct開源Alibaba	1,414	±7	11,219	—
39	—	mistral-large-3開源Mistral	1,413	±3	69,028	—
40	—	hunyuan-hy3-preview開源Tencent	1,413	±8	6,614	—
41	—	glm-4.5開源Z.ai	1,411	±5	23,712	—
42	—	qwen3.5-27b開源Alibaba	1,408	±4	27,227	—
43	—	Inkling Small開源Thinking Machines	1,405	±5	18,844	$0.52
44	—	qwen3-235b-a22b-no-thinking開源Alibaba	1,403	±5	37,389	—
45	—	longcat-flash-chat開源Meituan	1,401	±6	11,154	—
46	—	qwen3-235b-a22b-thinking-2507開源Alibaba	1,400	±7	8,796	—
47	—	qwen3-next-80b-a3b-instruct開源Alibaba	1,399	±5	22,529	—
48	—	deepseek-v3-0324開源DeepSeek · 另有 1 個檔位	1,396	±4	44,787	—
49	▲ 1	qwen3-vl-235b-a22b-thinking開源Alibaba	1,395	±7	7,780	—
50	▼ 1	qwen3.5-35b-a3b開源Alibaba	1,395	±4	29,043	—
51	—	step-3.5-flash開源StepFun	1,394	±4	57,137	—
52	—	mimo-v2-flash (non-thinking)開源Xiaomi · 另有 1 個檔位	1,392	±4	46,116	—
53	—	minimax-m2.5開源MiniMax	1,390	±4	40,843	—
54	—	qwen3-coder-480b-a35b-instruct開源Alibaba	1,388	±5	25,089	—
55	—	minimax-m2.1-preview開源MiniMax	1,384	±5	16,622	—
56	—	qwen3-30b-a3b-instruct-2507開源Alibaba	1,382	±5	23,222	—
57	▲ 1	glm-4.6v開源Z.ai	1,378	±11	2,762	—
58	▼ 1	trinity-large-preview開源 · 另有 1 個檔位	1,378	±4	29,742	—
59	—	qwen3-235b-a22b開源Alibaba	1,375	±5	25,865	—
60	—	glm-4.5-air開源Z.ai	1,373	±4	30,367	—
61	—	qwen3-next-80b-a3b-thinking開源Alibaba	1,369	±6	13,380	—
62	—	glm-4.7-flash開源Z.ai	1,366	±6	11,491	—
63	—	gemma-3-27b-it開源Google	1,365	±4	46,422	—
64	—	minimax-m1開源MiniMax	1,364	±4	34,030	—
65	—	nvidia-nemotron-3-super-120b-a12b開源NVIDIA	1,361	±7	7,458	—
66	—	mistral-small-2506開源Mistral	1,357	±5	17,391	—
67	—	intellect-3開源	1,356	±8	5,279	—
68	▲ 1	command-a-03-2025開源Cohere	1,354	±3	55,449	—
69	▲ 1	glm-4.5v開源Z.ai	1,353	±8	4,817	—
70	▲ 1	gpt-oss-120b開源OpenAI	1,352	±4	29,955	$0.26
71	▼ 3	nvidia-nemotron-3.5-lightning-30b-a3b-nvfp4開源NVIDIA	1,351	±7	8,338	—
72	—	step-3開源StepFun	1,349	±8	6,273	—
73	—	llama-3.1-nemotron-ultra-253b-v1開源NVIDIA	1,348	±12	2,549	—
74	—	qwen3-32b開源Alibaba	1,347	±9	3,926	—
75	—	minimax-m2開源MiniMax	1,346	±8	6,753	—
76	—	ling-flash-2.0開源Ant Group	1,344	±7	6,850	—
77	—	nvidia-llama-3.3-nemotron-super-49b-v1.5開源NVIDIA	1,343	±10	3,313	—
78	—	granite-4.2-30b開源IBM	1,342	±11	3,255	—
79	—	gemma-3-12b-it開源Google	1,342	±10	3,829	—
80	—	qwq-32b開源Alibaba · 另有 1 個檔位	1,336	±4	25,090	—
81	—	llama-3.1-405b-instruct-bf16開源Meta	1,335	±4	41,375	—
82	—	llama-3.1-405b-instruct-fp8開源Meta	1,333	±4	59,656	—
83	—	olmo-3.1-32b-instruct開源Ai2	1,330	±6	11,452	—
84	▲ 1	molmo-2-8b開源Ai2	1,328	±21	791	—
85	▼ 1	llama-3.3-nemotron-49b-super-v1開源NVIDIA	1,328	±12	2,218	—
86	▲ 1	llama-4-maverick-17b-128e-instruct開源Meta	1,327	±4	39,359	—
87	▼ 1	qwen3-30b-a3b開源Alibaba	1,327	±5	26,089	—
88	—	deepseek-v2.5-1210開源DeepSeek · 另有 1 個檔位	1,323	±8	6,795	—
89	—	llama-4-scout-17b-16e-instruct開源Meta	1,321	±5	29,740	—
90	—	ring-flash-2.0開源Ant Group	1,321	±7	6,899	—
91	—	qwen-max-0919開源Alibaba	1,318	±6	16,478	—
92	—	llama-3.3-70b-instruct開源Meta	1,318	±4	54,412	—
93	▲ 1	gpt-oss-20b開源OpenAI	1,317	±6	10,393	—
94	▼ 1	gemma-3n-e4b-it開源Google	1,317	±5	22,082	—
95	—	nvidia-nemotron-3-nano-30b-a3b-bf16開源NVIDIA	1,314	±6	15,325	—
96	—	mistral-large-2407開源Mistral · 另有 1 個檔位	1,314	±4	45,459	—
97	—	athene-v2-chat開源	1,314	±5	24,739	—
98	—	olmo-3-32b-think開源Ai2	1,307	±8	5,682	—
99	—	athene-70b-0725開源	1,306	±6	19,621	—
100	—	granite-4.1-8b開源IBM	1,306	±10	4,023	—
101	—	gemma-3-4b-it開源Google	1,303	±9	4,171	—
102	—	mistral-small-3.1-24b-instruct-2503開源Mistral	1,303	±5	32,618	—
103	—	qwen2.5-72b-instruct開源Alibaba	1,303	±4	39,406	—
104	—	llama-3.1-nemotron-70b-instruct開源NVIDIA	1,299	±8	7,140	—
105	—	granite-4.2-3b開源IBM	1,294	±12	3,072	—
106	—	llama-3.1-70b-instruct開源Meta	1,293	±4	55,240	—
107	—	jamba-1.5-large開源	1,289	±7	8,662	—
108	—	gemma-2-27b-it開源Google	1,289	±3	75,754	—
109	—	granite-4.2-8b開源IBM	1,287	±11	3,087	$0.11
110	—	llama-3.1-nemotron-51b-instruct開源NVIDIA	1,286	±10	3,749	—
111	—	llama-3.1-tulu-3-70b開源Ai2	1,286	±10	2,846	—
112	▲ 1	ibm-granite-h-small開源IBM	1,286	±8	5,532	—
113	▼ 1	olmo-3.1-32b-think開源Ai2	1,286	±7	8,117	—
114	—	gemma-2-9b-it-simpo開源	1,280	±7	10,072	—
115	—	nemotron-4-340b-instruct開源NVIDIA	1,277	±5	19,659	—
116	—	llama-3-70b-instruct開源Meta	1,276	±4	156,876	—
117	—	command-r-plus-08-2024開源Cohere	1,276	±7	9,866	—
118	—	mistral-small-24b-instruct-2501開源Mistral	1,274	±6	14,681	—
119	—	qwen2.5-coder-32b-instruct開源Alibaba	1,270	±8	5,432	—
120	—	c4ai-aya-expanse-32b開源Cohere	1,267	±5	27,124	—
121	—	gemma-2-9b-it開源Google	1,267	±4	54,611	—
122	—	deepseek-coder-v2開源DeepSeek	1,265	±6	15,147	—
123	—	qwen2-72b-instruct開源Alibaba	1,262	±5	37,325	—
124	—	command-r-plus開源Cohere	1,262	±4	77,554	—
125	—	phi-4開源Microsoft	1,256	±5	24,126	—
126	—	olmo-2-0325-32b-instruct開源Ai2	1,251	±11	3,334	—
127	—	command-r-08-2024開源Cohere	1,250	±7	10,140	—
128	—	jamba-1.5-mini開源	1,240	±7	8,858	—
129	—	ministral-8b-2410開源Mistral	1,237	±9	4,781	—
130	—	qwen1.5-110b-chat開源Alibaba	1,234	±6	26,195	—
131	—	qwen1.5-72b-chat開源Alibaba	1,233	±5	39,302	—
132	—	mixtral-8x22b-instruct-v0.1開源Mistral	1,229	±5	51,416	—
133	—	command-r開源Cohere	1,227	±5	54,036	—
134	—	llama-3-8b-instruct開源Meta	1,223	±4	104,642	—
135	—	c4ai-aya-expanse-8b開源Cohere	1,223	±7	9,818	—
136	—	llama-3.1-tulu-3-8b開源Ai2	1,220	±11	2,896	—
137	▲ 1	zephyr-orpo-141b-A35b-v0.1開源	1,213	±11	4,652	—
138	▼ 1	yi-1.5-34b-chat開源	1,213	±5	24,146	—
139	—	llama-3.1-8b-instruct開源Meta	1,211	±4	49,605	—
140	—	granite-3.1-8b-instruct開源IBM	1,208	±11	3,090	—
141	—	qwen1.5-32b-chat開源Alibaba	1,204	±6	21,741	—
142	—	gemma-2-2b-it開源Google	1,200	±4	46,616	—
143	—	phi-3-medium-4k-instruct開源Microsoft	1,198	±5	25,055	—
144	—	mixtral-8x7b-instruct-v0.1開源Mistral	1,197	±4	73,503	—
145	—	dbrx-instruct-preview開源	1,195	±6	32,191	—
146	—	qwen1.5-14b-chat開源Alibaba	1,191	±7	17,839	—
147	—	internlm2_5-20b-chat開源	1,191	±7	9,901	—
148	—	deepseek-llm-67b-chat開源DeepSeek	1,185	±11	4,932	—
149	—	wizardlm-70b開源Microsoft	1,184	±9	8,214	—
150	—	yi-34b-chat開源	1,183	±7	15,483	—
151	—	granite-3.0-8b-instruct開源IBM	1,183	±9	6,638	—
152	—	openchat-3.5開源 · 另有 1 個檔位	1,183	±10	7,968	—
153	—	gemma-1.1-7b-it開源Google	1,182	±6	23,893	—
154	—	snowflake-arctic-instruct開源	1,180	±6	32,832	—
155	—	granite-3.1-2b-instruct開源IBM	1,179	±11	3,188	—
156	—	tulu-2-dpo-70b開源	1,177	±10	6,535	—
157	—	openhermes-2.5-mistral-7b開源	1,176	±10	5,006	—
158	—	vicuna-33b開源	1,173	±6	22,479	—
159	—	starling-lm-7b-beta開源	1,171	±7	16,056	—
160	—	phi-3-small-8k-instruct開源Microsoft	1,171	±6	17,766	—
161	—	llama-2-70b-chat開源Meta	1,170	±5	38,492	—
162	—	starling-lm-7b-alpha開源	1,167	±8	10,224	—
163	—	llama-3.2-3b-instruct開源Meta	1,167	±8	7,936	—
164	—	nous-hermes-2-mixtral-8x7b-dpo開源	1,164	±12	3,777	—
165	—	granite-3.0-2b-instruct開源IBM	1,156	±8	6,837	—
166	—	llama2-70b-steerlm-chat開源NVIDIA	1,154	±13	3,585	—
167	—	solar-10.7b-instruct-v1.0開源	1,152	±13	4,155	—
168	—	dolphin-2.2.1-mistral-7b開源	1,152	±15	1,679	—
169	—	mpt-30b-chat開源	1,150	±12	2,572	—
170	—	wizardlm-13b開源Microsoft	1,149	±9	7,044	—
171	—	mistral-7b-instruct-v0.2開源Mistral	1,149	±7	19,402	—
172	—	falcon-180b-chat開源	1,148	±17	1,295	—
173	—	qwen1.5-7b-chat開源Alibaba	1,144	±10	4,737	—
174	—	phi-3-mini-4k-instruct-june-2024開源Microsoft	1,143	±6	12,297	—
175	▲ 1	vicuna-13b開源	1,141	±7	19,367	—
176	▼ 1	llama-2-13b-chat開源Meta	1,141	±7	19,174	—
177	—	qwen-14b-chat開源Alibaba	1,139	±11	4,964	—
178	—	gemma-7b-it開源Google	1,137	±9	8,925	—
179	—	codellama-34b-instruct開源Meta	1,137	±9	7,366	—
180	—	zephyr-7b-beta開源	1,131	±9	11,118	—
181	—	phi-3-mini-128k-instruct開源Microsoft	1,129	±7	20,685	—
182	—	phi-3-mini-4k-instruct開源Microsoft	1,128	±6	20,118	—
183	—	guanaco-33b開源	1,127	±12	2,921	—
184	—	zephyr-7b-alpha開源	1,127	±16	1,785	—
185	—	stripedhyena-nous-7b開源	1,121	±11	5,182	—
186	—	codellama-70b-instruct開源Meta	1,119	±18	1,143	—
187	—	gemma-1.1-2b-it開源Google	1,116	±8	10,854	—
188	—	vicuna-7b開源	1,115	±9	6,923	—
189	—	smollm2-1.7b-instruct開源	1,114	±14	2,199	—
190	—	llama-3.2-1b-instruct開源Meta	1,111	±8	8,045	—
191	—	mistral-7b-instruct開源Mistral	1,110	±9	8,977	—
192	—	llama-2-7b-chat開源Meta	1,108	±7	14,148	—
193	—	gemma-2b-it開源Google	1,093	±11	4,780	—
194	—	qwen1.5-4b-chat開源Alibaba	1,091	±9	7,597	—
195	—	olmo-7b-instruct開源Ai2	1,073	±11	6,328	—
196	—	koala-13b開源	1,071	±10	6,965	—
197	—	alpaca-13b開源	1,070	±11	5,745	—
198	—	gpt4all-13b-snoozy開源	1,067	±15	1,743	—
199	—	mpt-7b-chat開源	1,063	±12	3,924	—
200	—	chatglm3-6b開源	1,056	±12	4,658	—
201	—	RWKV-4-Raven-14B開源	1,042	±11	4,845	—
202	—	chatglm2-6b開源	1,024	±14	2,658	—
203	—	oasst-pythia-12b開源	1,023	±11	6,310	—
204	—	chatglm-6b開源	995	±13	4,914	—
205	—	fastchat-t5-3b開源	992	±12	4,203	—
206	—	dolly-v2-12b開源	982	±13	3,412	—
207	—	llama-13b開源Meta	975	±16	2,391	—
208	—	stablelm-tuned-alpha-7b開源	953	±13	3,287	—

其餘 158 個模型

智力進步

每家廠商當時最強的模型在 Artificial Analysis 智力指數上的分數，按模型釋出的月份畫。

漲得最多 · Anthropic +57.2目前最高 · Anthropic 64.6最高與最低相差 18.1 分時間範圍 2025年11月 – 2026年11月

64.6Anthropic60.0Google56.9OpenAI50.1Meta46.5Kimi