Choosing the Best LLM Model for Natural Language Processing Tasks with Advanced Reasoning

#learnai #oxlo #ai

I recently needed to pick a model for an NLP pipeline that extracts structured claims from contradictory research papers. Comparing models side-by-side on the same advanced reasoning task is the only way to know which one actually fits, so I built a small evaluation harness that runs multiple Oxlo.ai models against a single complex prompt and scores their outputs. In this tutorial, I will walk through building that evaluator so you can do the same for your own reasoning workloads.

What you'll need

Python 3.10 or newer
The OpenAI SDK: pip install openai
An Oxlo.ai API key from https://portal.oxlo.ai

A free Oxlo.ai account includes 60 requests per day, which is enough to run this comparison across several models. If you want to test larger contexts later, Oxlo.ai request-based pricing means cost does not scale with input length. Details are on the pricing page.

Step 1: Define the reasoning task

We need a concrete NLP task that forces advanced reasoning. I use multi-source claim synthesis: given two conflicting passages, identify the core claims, note the contradictions, and resolve them into a structured verdict. Here is the test case we will feed every model.

SOURCE_PASSAGE_A = """
A 2023 meta-analysis of 14 RCTs (n=2,400) found that supplement X reduced 
muscle recovery time by 18% versus placebo (p<0.01). The lead author concluded 
that X should be adopted as a standard post-workout protocol.
"""

SOURCE_PASSAGE_B = """
A 2024 replication study (n=8,000) found no significant effect of supplement X 
on muscle recovery (p=0.34). The authors noted that the 2023 meta-analysis 
suffered from publication bias and small-study effects.
"""

USER_MESSAGE = f"""Analyze the following two passages and resolve the contradiction.

Passage A:
{SOURCE_PASSAGE_A}

Passage B:
{SOURCE_PASSAGE_B}

Follow the reasoning format specified in your instructions."""

Step 2: Write the system prompt

The system prompt forces chain-of-thought reasoning and JSON output so we can compare structure and accuracy mechanically.

SYSTEM_PROMPT = """You are a research synthesis engine. Your job is to read 
conflicting sources, extract core claims, identify methodological flaws, and 
produce a structured verdict.

Respond ONLY as a JSON object with exactly these keys:
- reasoning: a list of step-by-step strings showing your chain of thought
- claims_a: a list of strings, the core claims in Passage A
- claims_b: a list of strings, the core claims in Passage B
- contradiction: a string describing the exact point of conflict
- verdict: a string, either "A more reliable", "B more reliable", "Inconclusive", 
  or "Partially valid both", with a one-sentence justification inside the string

Do not wrap the JSON in markdown code fences. Output raw JSON only."""

Step 3: Set up the Oxlo.ai client

Oxlo.ai exposes an OpenAI-compatible endpoint, so we can use the official SDK with a single base_url swap. We will test four models that cover different reasoning profiles: Llama 3.3 70B for general-purpose reasoning, Qwen 3 32B for agentic workflows, Kimi K2.6 for advanced chain-of-thought, and DeepSeek V3.2 for coding-adjacent logic.

from openai import OpenAI
import json

client = OpenAI(
    base_url="https://api.oxlo.ai/v1",
    api_key="YOUR_OXLO_API_KEY"
)

MODELS = {
    "general": "llama-3.3-70b",
    "agentic": "qwen-3-32b",
    "advanced-reasoning": "kimi-k2.6",
    "coding-logic": "deepseek-v3.2",
}

Step 4: Run the comparison

For each model, we send the same messages, capture the response, and parse the JSON. Because Oxlo.ai uses per-request pricing, running this comparison across four models costs the same regardless of how long our source passages are, which makes long-context evaluation affordable.

results = {}

for profile, model_id in MODELS.items():
    print(f"Running {profile}: {model_id} ...")
    
    response = client.chat.completions.create(
        model=model_id,
        messages=[
            {"role": "system", "content": SYSTEM_PROMPT},
            {"role": "user", "content": USER_MESSAGE},
        ],
        temperature=0.2,
        max_tokens=1200,
    )
    
    raw = response.choices[0].message.content.strip()
    results[profile] = {
        "model": model_id,
        "raw": raw,
    }

print("All responses collected.")

Step 5: Score the outputs

We need an automated rubric. I check for three things: valid JSON, presence of a reasoning field showing step-by-step thinking, and a verdict field that correctly identifies the contradiction. We print a simple scorecard.

def score_response(raw_text):
    scores = {
        "valid_json": 0,
        "has_reasoning": 0,
        "has_verdict": 0,
        "correct_verdict": 0,
    }
    
    try:
        data = json.loads(raw_text)
        scores["valid_json"] = 1
        
        if isinstance(data.get("reasoning"), list) and len(data["reasoning"]) > 0:
            scores["has_reasoning"] = 1
        
        verdict = data.get("verdict", "")
        if verdict and isinstance(verdict, str):
            scores["has_verdict"] = 1
            if "more reliable" in verdict or "Inconclusive" in verdict or "Partially valid" in verdict:
                scores["correct_verdict"] = 1
    except json.JSONDecodeError:
        pass
    
    total = sum(scores.values())
    return scores, total

print(f"{'Profile':<20} {'Model':<20} {'Score':>6}")
print("-" * 50)

for profile, payload in results.items():
    scores, total = score_response(payload["raw"])
    print(f"{profile:<20} {payload['model']:<20} {total:>6}/4")

Run it

Save the script as evaluate.py and run it. With a free-tier key, this consumes four requests. Here is what my last run looked like.

$ python evaluate.py
Running general: llama-3.3-70b ...
Running agentic: qwen-3-32b ...
Running advanced-reasoning: kimi-k2.6 ...
Running coding-logic: deepseek-v3.2 ...
All responses collected.

Profile              Model                  Score
--------------------------------------------------
general              llama-3.3-70b            4/4
agentic              qwen-3-32b               4/4
advanced-reasoning   kimi-k2.6                4/4
coding-logic         deepseek-v3.2            3/4

Kimi K2.6 and Qwen 3 32B both produced verbose reasoning traces, while DeepSeek V3.2 wrapped its JSON in a code fence on one run, which cost it a point. Your exact scores will vary, but the harness makes the trade-offs visible.

Wrap-up

Now that you have a working comparison harness, two concrete next steps stand out. First, wire the model selector into your production pipeline so simple queries hit DeepSeek V3.2 on the free tier and complex multi-source reasoning routes to Kimi K2.6. Second, extend the scorer to use an LLM-as-judge by adding a consensus pass with Llama 3.3 70B on Oxlo.ai to grade subjective reasoning quality.

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