Chemistry produces a massive spectrum of structured and unstructured data, from SMILES strings and InChI identifiers to multi-step synthesis protocols and decades of patent literature. Applying large language models to this domain requires more than simple prompt engineering. It demands long-context parsing, structured output, multi-modal understanding, and agentic tool use. These capabilities are now available through modern open-source models, but chemistry workloads often involve unusually long inputs that drive up costs on traditional token-based inference providers. Oxlo.ai addresses this with a developer-first, request-based pricing model and a fully OpenAI-compatible API that supports more than 45 models across seven categories.
Structured Molecular Reasoning with JSON Mode
Translating between molecular representations is a common bottleneck. An LLM can parse a SMILES string, suggest an IUPAC name, identify functional groups, or flag potential safety concerns, but only if the output is machine readable. Oxlo.ai supports JSON mode across its chat and reasoning models, including Qwen 3 32B and Llama 3.3 70B, so you can constrain responses to a predictable schema.
import openai
client = openai.OpenAI(
base_url="https://api.oxlo.ai/v1",
api_key="YOUR_OXLO_API_KEY"
)
response = client.chat.completions.create(
model="qwen3-32b",
messages=[{
"role": "user",
"content": (
"Parse the SMILES CC(=O)Oc1ccccc1C(=O)O and return a JSON object "
"with fields: iupac_name, molecular_formula, functional_groups."
)
}],
response_format={"type": "json_object"}
)
print(response.choices[0].message.content)
Because Oxlo.ai charges one flat cost per API request regardless of prompt length, you can include extensive system instructions, few-shot examples, or safety context without inflating the price. On token-based platforms such as Together AI, Fireworks AI, OpenRouter, Replicate, or Anyscale, adding that context would scale the bill proportionally.
Long-Context Reaction Pathways
Reaction prediction and retrosynthesis are inherently reasoning-heavy tasks. A single prompt might contain a dozen example transformations, electron-pushing rules, and stereochemical constraints. Models such as DeepSeek R1 671B MoE, Kimi K2.6, and DeepSeek V4 Flash excel at deep reasoning and near state-of-the-art open-source inference, making them strong candidates for these workflows.
The challenge is economic, not just technical. A long few-shot prompt with detailed reaction schemas can quickly accumulate input tokens. With Oxlo.ai, the cost remains a single flat request. This predictability matters when you are iterating on a synthesis planner or running batch evaluations across a library of substrates. Competitors that bill by the token will charge more as your prompts grow, which discourages the rich context that actually improves chemical reasoning.
Embedding Scientific Literature
Retrieval-augmented generation over scientific literature requires embedding dense technical text. Patents, experimental sections, and materials safety data sheets are often far longer than standard web articles. Oxlo.ai offers embedding models such as BGE-Large and E5-Large through a dedicated embeddings endpoint.
response = client.embeddings.create(
model="bge-large",
input=(
"The palladium-catalyzed Buchwald-Hartwig amination couples aryl halides "
"with amines to form carbon-nitrogen bonds under mild conditions..."
)
)
vector = response.data[0].embedding
Because each embedding call is billed as one request, embedding a long experimental procedure costs the same as embedding a short sentence. For chemistry teams building RAG pipelines over large corpora, that pricing structure removes the penalty for using full-text documents instead of truncated abstracts.
Vision-Enabled Lab Analysis
Chemistry is not purely textual. Thin-layer chromatography plates, mass spectra, NMR charts, and microscopy images all contain actionable information. Oxlo.ai supports vision input through models such as Gemma 3 27B and Kimi VL A3B, allowing you to pass image data directly into the chat completions pipeline.
response = client.chat.completions.create(
model="gemma-3-27b",
messages=[{
"role": "user",
"content": [
{
"type": "text",
"text": "Estimate the Rf values and describe the spot intensities in this TLC plate."
},
{
"type": "image_url",
"image_url": {"url": "data:image/png;base64,iVBORw0KGgo..."}
}
]
}]
)
This multi-modal capability turns a standard API pipeline into a lab assistant that can read both protocols and their physical outputs. There are no cold starts on popular models, so the turnaround between uploading an image and receiving an analysis feels instantaneous during iterative lab work.
Agentic Workflows and Tool Use
The most useful chemistry applications do not stop at text generation. They invoke calculators, query compound databases, or call robotic synthesis APIs. Oxlo.ai supports function calling and tool use across its LLM catalog, including agent-focused models such as GLM 5, Minimax M2.5, and DeepSeek V3.2.
tools = [{
"type": "function",
"function": {
"name": "get_molecular_weight",
"description": "Calculate the molecular weight for a SMILES string.",
"parameters": {
"type": "object",
"properties": {
"smiles": {"type": "string"}
},
"required": ["smiles"]
}
}
}]
response = client.chat.completions.create(
model="deepseek-v3.2",
messages=[{
"role": "user",
"content": "What is the molecular weight of CC(C)Cc1ccc(C(C)C(=O)O)cc1?"
}],
tools=tools
)
Combined with multi-turn conversation support, you can build agents that plan a synthesis, verify each step against an external knowledge graph, and revise the route based on real-time feedback. Because Oxlo.ai is fully OpenAI SDK compatible, you can drop this logic into an existing Python or Node.js codebase by changing only the base URL.
Infrastructure and Cost Predictability
Chemistry workloads are uniquely expensive on token-based infrastructure. A single request might concatenate a lengthy system prompt, a detailed experimental protocol, a long SMILES representation, and a multi-turn conversation history. On providers that bill per token, that input length directly multiplies the cost. Oxlo.ai’s request-based pricing can be 10-100x cheaper for long-context workloads because the price is flat per API call, regardless of how much text or how many images you include.
The platform offers a Free tier with 60 requests per day and a 7-day full-access trial, a Pro plan at $80 per month for 1,000 requests per day, a Premium plan at $350 per month for 5,000 requests per day with priority queue access, and custom Enterprise contracts with dedicated GPUs. For exact details, see the Oxlo.ai pricing page.
With no cold starts on popular models, streaming responses, and full OpenAI API compatibility, Oxlo.ai removes the infrastructure friction that typically slows down scientific experimentation. If you are building chemical informatics pipelines, the flat pricing model lets you focus on model behavior and data quality instead of token arithmetic.
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