Cohere Rerank 3.5 vs OLMo 2 13B
Compare Cohere Rerank 3.5 and OLMo 2 13B: pricing, performance, context window, latency, and best use cases. Side-by-side comparison on XALEN.
Updated 2026-05-21 · By Abhishek Raj · Our methodology
| Feature | Cohere Rerank 3.5 | OLMo 2 13B |
|---|---|---|
| Category | Reranking | Open Source |
| Parameters | ~600M | 13B |
| Context Window | 4K | 32K |
| Input Price | $0.002/search/1M tokens | $0.015/1M tokens |
| Output Price | N/A/1M tokens | $0.03/1M tokens |
| Latency | ~25ms | ~120ms |
Choose Cohere Rerank 3.5 when:
- ✓ Search reranking
- ✓ RAG improvement
- ✓ Result quality
Higher quality, Multilingual, Fast
Choose OLMo 2 13B when:
- ✓ Research
- ✓ Custom training
- ✓ Transparency-required apps
Fully open (weights + data), Transparent, Research-friendly
Verdict: Cohere Rerank 3.5 vs OLMo 2 13B
For cost efficiency, Cohere Rerank 3.5 wins at $0.002/search/1M input tokens. For speed, OLMo 2 13B is faster at ~120ms. Cohere Rerank 3.5 excels at Search reranking while OLMo 2 13B is better for Research. Both are available on XALEN through a single API — try them in the Playground to see which fits your workload.
Detailed Analysis
Pricing Comparison
Cohere Rerank 3.5 costs $0.002/search/1M input tokens and N/A/1M output tokens. OLMo 2 13B costs $0.015 input and $0.03 output. Cohere Rerank 3.5 is 7.5x cheaper on input tokens. XALEN offers batch processing at 50% discount on both models.
Performance & Context
Cohere Rerank 3.5 has a 4K context window with ~25ms latency. OLMo 2 13B offers 32K context at ~120ms. OLMo 2 13B has the larger context window.
Best For
Cohere Rerank 3.5 (Reranking) is optimized for: Search reranking, RAG improvement, Result quality. OLMo 2 13B (Open Source) works best for: Research, Custom training, Transparency-required apps.
Try Both on XALEN
Both models are available through XALEN's OpenAI-compatible API. Switch between them by changing the model parameter:
from xalen import XALEN
client = XALEN(api_key="xln_test_YOUR_KEY")
# Use Cohere Rerank 3.5
response_a = client.chat.completions.create(
model="rerank-v3-5",
messages=[{"role": "user", "content": "Your question here"}]
)
# Use OLMo 2 13B
response_b = client.chat.completions.create(
model="olmo-2-13b",
messages=[{"role": "user", "content": "Your question here"}]
)Frequently Asked Questions
Which is better, Cohere Rerank 3.5 or OLMo 2 13B?
Cohere Rerank 3.5 (Reranking, ~600M) offers Higher quality. OLMo 2 13B (Open Source, 13B) offers Fully open (weights + data). Choose Cohere Rerank 3.5 for Search reranking or OLMo 2 13B for Research.
How much does Cohere Rerank 3.5 cost vs OLMo 2 13B?
Cohere Rerank 3.5: $0.002/search/1M input, N/A/1M output. OLMo 2 13B: $0.015/1M input, $0.03/1M output. Both available on XALEN with batch processing at 50% discount.
Can I use both models on XALEN?
Yes. XALEN provides 200+ models through a single OpenAI-compatible API. Switch between Cohere Rerank 3.5 and OLMo 2 13B by changing the model parameter. No code changes needed.
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Last updated: 2026-05-21. Pricing and specifications may change. Check pricing page for latest rates.