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Same-model provider benchmark

CoreWeave vs Venice: LLM provider comparison

Compare CoreWeave and Venice on 12 exact shared text models. ProviderBench keeps speed, price, and catalog coverage separate so naturally faster model catalogs cannot distort the result.

CoreWeave

20 indexed models

Headquarters
United States
Server regions
United States
Model types
20 text

Venice

30 indexed models

Headquarters
United States
Server regions
United States
Model types
30 text

At a glance

Metric winners

There is no overall score. Each winner answers one specific question using only directly comparable data.

Fastest on shared models

1.14× typical advantage

12 exact models with complete recent speed data

Lowest token cost

$1.2121 / 1M

12 exact models using a 1K-input/500-output mix

Most models available

30 models

Complete catalog coverage across all indexed modalities

Shared-model benchmark summary

MetricCoreWeave(0)Venice(0)
SpeedCoreWeave8.20 sVenice11.32 s
TTFTCoreWeave0.44 sVenice1.17 s
TPSCoreWeave66.0 tok/sVenice49.0 tok/s
UptimeCoreWeave99.92%Venice99.82%
BlendedCoreWeave$1.3297 / 1MVenice$1.2121 / 1M
Green value Better comparable resultRed value Worse comparable result

Visual comparison

Price and performance charts

CoreWeaveVenice
500-token response by shared model

Estimated seconds using recent median response-start and output-speed data. Lower is better.

CoreWeave has the lower typical same-model response ratio across 12 measured models.

Blended token price by shared model

USD per 1 million tokens using a 1,000-input/500-output mix. Lower is better.

Venice has the lower typical price ratio across 12 priced shared models.

Shared text models

12 exact models · newest first

ModelCoreWeave(0)Venice(0)
GLM 5.2Winner · Venice
CoreWeave
Speed— Worst comparable value
20.46 s
TTFT— Worst comparable value
1.94 s
TPS— Worst comparable value
27.0 tok/s
Uptime— Best comparable value
99.96%
Context
262.1K
Route
coreweave/fp4
Blended— Best comparable value
$2.3933 / 1M
Venice
Speed— Best comparable value
16.70 s
TTFT— Best comparable value
1.55 s
TPS— Best comparable value
33.0 tok/s
Uptime— Worst comparable value
99.93%
Context
1M
Route
venice/fp8
Blended— Worst comparable value
$2.4 / 1M
Kimi K2.7 CodeWinner · CoreWeave
CoreWeave
Speed— Best comparable value
7.35 s
TTFT— Best comparable value
0.68 s
TPS— Best comparable value
75.0 tok/s
Uptime
100.00%
Context
262.1K
Route
coreweave/int4
Blended— Worst comparable value
$1.96 / 1M
Venice
Speed— Worst comparable value
9.56 s
TTFT— Worst comparable value
1.81 s
TPS— Worst comparable value
64.5 tok/s
Uptime
N/a
Context
256K
Route
venice/int4
Blended— Best comparable value
$1.6667 / 1M
Qwen3.6 35B A3BWinner · Venice
CoreWeave
Speed— Worst comparable value
3.72 s
TTFT— Best comparable value
0.26 s
TPS— Worst comparable value
144.5 tok/s
Uptime
100.00%
Context
262.1K
Route
coreweave/fp8
Blended— Worst comparable value
$0.5833 / 1M
Venice
Speed— Best comparable value
2.91 s
TTFT— Worst comparable value
0.57 s
TPS— Best comparable value
214.0 tok/s
Uptime
100.00%
Context
256K
Route
venice/fp8
Blended— Best comparable value
$0.4333 / 1M
Qwen3.6 27BWinner · Venice
CoreWeave
Speed— Worst comparable value
18.18 s
TTFT— Best comparable value
0.33 s
TPS— Worst comparable value
28.0 tok/s
Uptime— Best comparable value
100.00%
Context
262.1K
Route
coreweave/fp8
Blended— Worst comparable value
$1.6 / 1M
Venice
Speed— Best comparable value
9.77 s
TTFT— Worst comparable value
0.68 s
TPS— Best comparable value
55.0 tok/s
Uptime— Worst comparable value
99.71%
Context
256K
Route
venice/fp8
Blended— Best comparable value
$1.3 / 1M
DeepSeek V4 ProWinner · Venice
CoreWeave
Speed— Worst comparable value
64.00 s
TTFT— Worst comparable value
1.50 s
TPS— Worst comparable value
8.0 tok/s
Uptime— Best comparable value
98.59%
Context
1M
Route
coreweave/fp8
Blended— Worst comparable value
$2.32 / 1M
Venice
Speed— Best comparable value
12.87 s
TTFT— Best comparable value
1.24 s
TPS— Best comparable value
43.0 tok/s
Uptime— Worst comparable value
95.12%
Context
1M
Route
venice
Blended— Best comparable value
$2.2003 / 1M
DeepSeek V4 FlashWinner · Venice
CoreWeave
Speed— Best comparable value
17.27 s
TTFT— Best comparable value
0.60 s
TPS— Worst comparable value
30.0 tok/s
Uptime— Best comparable value
99.85%
Context
1M
Route
coreweave/fp8
Blended— Worst comparable value
$0.1867 / 1M
Venice
Speed— Worst comparable value
17.53 s
TTFT— Worst comparable value
1.90 s
TPS— Best comparable value
32.0 tok/s
Uptime— Worst comparable value
98.80%
Context
1M
Route
venice
Blended— Best comparable value
$0.1837 / 1M
Kimi K2.6Winner · CoreWeave
CoreWeave
Speed— Best comparable value
3.30 s
TTFT— Best comparable value
0.34 s
TPS— Best comparable value
169.0 tok/s
Uptime— Worst comparable value
99.89%
Context
262.1K
Route
coreweave/fp4
Blended— Worst comparable value
$1.9667 / 1M
Venice
Speed— Worst comparable value
13.48 s
TTFT— Worst comparable value
0.98 s
TPS— Worst comparable value
40.0 tok/s
Uptime— Best comparable value
100.00%
Context
256K
Route
venice/int4
Blended— Best comparable value
$1.6667 / 1M
GLM 5.1Winner · CoreWeave
CoreWeave
Speed— Best comparable value
4.38 s
TTFT— Best comparable value
0.50 s
TPS— Best comparable value
129.0 tok/s
Uptime
100.00%
Context
202.8K
Route
coreweave/fp8
Blended— Best comparable value
$2.4 / 1M
Venice
Speed— Worst comparable value
7.55 s
TTFT— Worst comparable value
1.30 s
TPS— Worst comparable value
80.0 tok/s
Uptime
100.00%
Context
200K
Route
venice/fp8
Blended— Worst comparable value
$2.64 / 1M
Gemma 4 31BWinner · Venice
CoreWeave
Speed— Worst comparable value
30.82 s
TTFT— Worst comparable value
1.41 s
TPS— Worst comparable value
17.0 tok/s
Uptime— Worst comparable value
77.67%
Context
262.1K
Route
coreweave/bf16
Blended— Best comparable value
$0.1967 / 1M
Venice
Speed— Best comparable value
23.82 s
TTFT— Best comparable value
1.10 s
TPS— Best comparable value
22.0 tok/s
Uptime— Best comparable value
98.68%
Context
256K
Route
venice/bf16
Blended— Worst comparable value
$0.2 / 1M
Qwen3.5-35B-A3BWinner · CoreWeave
CoreWeave
Speed— Best comparable value
4.86 s
TTFT— Best comparable value
0.35 s
TPS— Best comparable value
111.0 tok/s
Uptime
100.00%
Context
262.1K
Route
coreweave/fp8
Blended— Best comparable value
$0.5833 / 1M
Venice
Speed— Worst comparable value
6.76 s
TTFT— Worst comparable value
1.04 s
TPS— Worst comparable value
87.5 tok/s
Uptime
N/a
Context
256K
Route
venice
Blended— Worst comparable value
$0.625 / 1M
MiniMax M2.5Winner · CoreWeave
CoreWeave
Speed— Best comparable value
6.55 s
TTFT— Best comparable value
0.38 s
TPS— Worst comparable value
81.0 tok/s
Uptime
100.00%
Context
196.6K
Route
coreweave/fp8
Blended— Worst comparable value
$0.6 / 1M
Venice
Speed— Worst comparable value
9.64 s
TTFT— Worst comparable value
4.37 s
TPS— Best comparable value
95.0 tok/s
Uptime
N/a
Context
198K
Route
venice
Blended— Best comparable value
$0.4967 / 1M
Qwen3 Coder 480B A35BWinner · CoreWeave
CoreWeave
Speed— Best comparable value
9.06 s
TTFT— Best comparable value
0.29 s
TPS— Best comparable value
57.0 tok/s
Uptime
N/a
Context
262.1K
Route
coreweave/bf16
Blended— Worst comparable value
$1.1667 / 1M
Venice
Speed— Worst comparable value
16.67 s
TTFT— Worst comparable value
1.05 s
TPS— Worst comparable value
32.0 tok/s
Uptime
100.00%
Context
256K
Route
venice/fp8
Blended— Best comparable value
$0.7333 / 1M

A per-model winner combines blended price and estimated 500-token response time with equal proportional weight. Ties and rows missing either measurement receive no badge. Comparison data calculated . Values use one deterministic route per provider and model; missing measurements remain visible as N/a.

CoreWeave vs Venice analysis

How CoreWeave and Venice compare for AI inference

CoreWeave and Venice share 12 indexed text models, including GLM 5.2, Kimi K2.7 Code, Qwen3.6 35B A3B, Qwen3.6 27B, DeepSeek V4 Pro. CoreWeave has the stronger typical response-time result on the directly measured set.

Same-model speed evidence

12 shared models currently have complete response-start and output-speed measurements on both providers. The speed comparison uses per-model ratios before taking the median, so naturally faster model catalogs do not improve the result.

Token pricing on one workload

12 shared models have complete input and output prices on both providers. Prices use the same 1,000-input/500-output-token mix and are normalized to one million tokens for readability.

Catalog and deployment differences

CoreWeave has 20 indexed models and lists 1 published server region; Venice has 30 models and lists 1 region. Verify data residency, privacy terms, limits, and production latency directly before choosing.