DeepInfra
87 indexed models
- Headquarters
United States
- Server regions
- N/a
- Model types
- 67 text, 15 embeddings, 5 speech
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Same-model provider benchmark
Compare DeepInfra and Venice on 27 exact shared text models. ProviderBench keeps speed, price, and catalog coverage separate so naturally faster model catalogs cannot distort the result.
87 indexed models
29 indexed models
At a glance
There is no overall score. Each winner answers one specific question using only directly comparable data.
| Metric | DeepInfra | Venice |
|---|---|---|
| Typical 500-token response | 17.92 s | 15.58 s |
| Typical response start | 0.75 s | 1.19 s |
| Typical output speed | 29.0 tok/s | 35.0 tok/s |
| Blended token price | $0.8127 / 1M | $1.0168 / 1M |
| Typical route uptime | 99.24% | 99.42% |
Visual comparison
Estimated seconds using recent median response-start and output-speed data. Lower is better.
Venice has the lower typical same-model response ratio across 26 measured models.
USD per 1 million tokens using a 1,000-input/500-output mix. Lower is better.
DeepInfra has the lower typical price ratio across 27 priced shared models.
| Model | DeepInfra | Venice |
|---|---|---|
GLM 5.2Winner · Venice z-ai/glm-5.2 | DeepInfra
| Venice
|
Kimi K2.7 CodeWinner · Venice moonshotai/kimi-k2.7-code | DeepInfra
| Venice
|
Nemotron 3 UltraWinner · DeepInfra nvidia/nemotron-3-ultra-550b-a55b | DeepInfra
| Venice
|
MiniMax M3Winner · Venice minimax/minimax-m3 | DeepInfra
| Venice
|
Qwen3.6 27BWinner · Venice qwen/qwen3.6-27b | DeepInfra
| Venice
|
DeepSeek V4 ProWinner · DeepInfra deepseek/deepseek-v4-pro | DeepInfra
| Venice
|
DeepSeek V4 FlashWinner · DeepInfra deepseek/deepseek-v4-flash | DeepInfra
| Venice
|
MiMo-V2.5Winner · Venice xiaomi/mimo-v2.5 | DeepInfra
| Venice
|
Kimi K2.6Winner · Venice moonshotai/kimi-k2.6 | DeepInfra
| Venice
|
GLM 5.1Winner · DeepInfra z-ai/glm-5.1 | DeepInfra
| Venice
|
Gemma 4 26B A4BWinner · DeepInfra google/gemma-4-26b-a4b-it | DeepInfra
| Venice
|
Gemma 4 31BWinner · Venice google/gemma-4-31b-it | DeepInfra
| Venice
|
qwen/qwen3.5-9b | DeepInfra
| Venice
|
Qwen3.5-35B-A3BWinner · DeepInfra qwen/qwen3.5-35b-a3b | DeepInfra
| Venice
|
Qwen3.5 397B A17BWinner · DeepInfra qwen/qwen3.5-397b-a17b | DeepInfra
| Venice
|
MiniMax M2.5Winner · Venice minimax/minimax-m2.5 | DeepInfra
| Venice
|
GLM 5Winner · DeepInfra z-ai/glm-5 | DeepInfra
| Venice
|
Kimi K2.5Winner · Venice moonshotai/kimi-k2.5 | DeepInfra
| Venice
|
GLM 4.7 FlashWinner · DeepInfra z-ai/glm-4.7-flash | DeepInfra
| Venice
|
GLM 4.7Winner · DeepInfra z-ai/glm-4.7 | DeepInfra
| Venice
|
DeepSeek V3.2Winner · DeepInfra deepseek/deepseek-v3.2 | DeepInfra
| Venice
|
GLM 4.6Winner · DeepInfra z-ai/glm-4.6 | DeepInfra
| Venice
|
Qwen3 VL 235B A22B InstructWinner · DeepInfra qwen/qwen3-vl-235b-a22b-instruct | DeepInfra
| Venice
|
Qwen3 235B A22B Thinking 2507Winner · DeepInfra qwen/qwen3-235b-a22b-thinking-2507 | DeepInfra
| Venice
|
Qwen3 Coder 480B A35BWinner · DeepInfra qwen/qwen3-coder | DeepInfra
| Venice
|
Qwen3 235B A22B Instruct 2507Winner · DeepInfra qwen/qwen3-235b-a22b-2507 | DeepInfra
| Venice
|
Mistral Small 3.2 24BWinner · DeepInfra mistralai/mistral-small-3.2-24b-instruct | DeepInfra
| Venice
|
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.
DeepInfra vs Venice analysis
DeepInfra and Venice share 27 indexed text models, including GLM 5.2, Kimi K2.7 Code, Nemotron 3 Ultra, MiniMax M3, Qwen3.6 27B. Venice has the stronger typical response-time result on the directly measured set.
26 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.
27 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.
DeepInfra has 87 indexed models and lists no published server regions; Venice has 29 models and lists 1 region. Verify data residency, privacy terms, limits, and production latency directly before choosing.
Related same-model benchmarks
Explore qualified alternatives with the most shared measured models. Recommendations include comparisons for both DeepInfra and Venice.