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Ollama benchmark

Continuous, automated speed tests for every Ollama Cloud model. One streaming request per model every ~10 minutes. No cherry-picked results — just raw measurements from outside Ollama's network.

Top 10 fastest Ollama Cloud models right now

Rank Model TPS now TPS 24h avg TTFT Reliability
#1 DeepSeek V4 Flash 258.1 191.8 538ms 99%
#2 DeepSeek V4 Flash 0731 135.1 147.0 5.2s 100%
#3 DeepSeek V4 Pro 124.4 116.3 672ms 100%
#4 GPT-OSS 20B 116.8 116.4 445ms 100%
#5 Nemotron 3 Nano 30B 112.8 147.8 445ms 96%
#6 Gemma4 31B 112.0 88.6 332ms 99%
#7 Nemotron 3 Nano 30B 99.9 148.4 445ms 99%
#8 GLM 5.1 97.5 88.2 933ms 100%
#9 GPT-OSS 120B 94.3 105.5 484ms 92%
#10 Kimi K3 88.9 69.8 1.1s 100%

See all 25 models on the leaderboard →

What the Ollama benchmark measures

Each benchmark run sends a single streaming chat-completion request to the Ollama Cloud API endpoint. The model is prompted to write a 400-word prose explanation of HTTP request routing, with a max_tokens cap of 300.

TPS — tokens per second
Generation throughput: output tokens divided by the time between first and last token. Excludes TTFT so TPS reflects pure decode speed, not queue or prompt-processing delay.
TTFT — time to first token
Milliseconds from request dispatch to the first content chunk in the stream. Captures network round-trip plus the provider's prompt-processing latency.
Reliability
Percentage of benchmark runs that succeeded in the last 24 hours. Failures are classified as auth, rate_limit, server, timeout, network, or malformed.

Benchmark cadence and fairness

The worker uses a priority queue that always picks the most-overdue (provider, model) pair, targeting a ~10-minute interval per model. Benchmarks run sequentially — one request at a time — mirroring realistic single-client usage.

We benchmark on the Ollama Cloud premium plan. This gives full catalog access including models behind the paywall. Speed numbers reflect premium-tier infrastructure, not free-tier which may be slower under load.

Full methodology →

How to read the numbers

  • TPS is relative, not absolute. The same model can vary 20–30% across hours depending on provider load and time of day. Use the 24h average for a more stable comparison.
  • TTFT matters for interactive use. A model with high TPS but 3 s TTFT feels slow in a chat interface. The leaderboard sorts by latest TPS by default — sort by TTFT to optimise for responsiveness.
  • Reliability is often the deciding factor. A model that returns errors 30% of the time needs retry logic in production. Filter for ≥90% reliability for production workloads.