Needle 26M vs Qwen3-0.6B: A Real CPU Function-Call Benchmark

Two open-weight tool-calling models. Same 50 queries, same CPU, same rubric. One is 23× smaller. The interesting finding isn't who wins, it's that the two models fail in completely different ways.

Two models, two completely different ways to fail

One picks the wrong tool. The other doesn't pick at all.

That's the actual story of this benchmark. We ran Needle (26M), a function-call specialist distilled from Gemini 3.1, head-to-head against Qwen3-0.6B, Alibaba's smallest general-purpose model. Same 50 structured queries across five difficulty tiers, CPU-only, evaluated with the same rubric.

Then, because the most obvious criticism of any tiny-vs-bigger comparison is "you didn't prompt the bigger model well enough," we ran Qwen3 a second time with a strong system prompt that forces it to always emit a tool call. That gives us three columns to compare:

The short version of what we found:

Three takeaways before we get into the numbers:

1. A well-prompted Qwen3-0.6B beats Needle on accuracy by 12 points (84% vs 72%). The criticism of the original "default Qwen3" run was fair, a lot of Qwen3's failures were prompt-engineering failures, not capability failures.
2. Needle is still 4.2× faster on CPU. A 23×-smaller model that finishes in a quarter of the time is a different product category, that gap doesn't go away with prompting.
3. The strong prompt creates a new failure mode for Qwen3. It now calls a tool too eagerly, including answering "What's 2+2?" by calling run_command("2+2", timeout=30). Needle correctly emits no tool call there.

This is the real shape of the choice between these two models. Read on for the breakdown.

The benchmark

The test set

Fifty queries, ten per tier:

Five mock tools: get_weather, search_web, create_file, run_command, get_time.

The rubric

Three booleans per run:

Hardware & protocol

4-core CPU, no GPU (CUDA_VISIBLE_DEVICES=""). Python 3.12, transformers 4.50+, torch 2.4+, jax 0.4.30 / flax 0.8.5 for Needle. One discarded warmup query per model. 50 queries × 3 model variants = 150 timed runs.

The numbers

Overall

Prompted Qwen3 wins accuracy. Needle wins latency by ~4×, and that's on a model 23× smaller.

Accuracy by tier

The single most dramatic line: T3 jumps from 10% → 90% just by prompting Qwen3 properly. That entire class of failure, Qwen3 answering implicit queries in prose instead of calling the tool, disappears with a four-sentence system prompt.

The hidden cost: T5 wins for prompted Qwen3 are partly over-calling. On "What's 2+2?", prompted Qwen3 routes to run_command("2+2", timeout=30), a tool call where none was expected. The original Qwen3 (and Needle) correctly emit no tool here.

Latency by tier (mean ms)

Prompted Qwen3 doesn't get faster than default Qwen3, the prompt only fixes whether a tool is called, not how long the model spends thinking. Needle holds a 3.3×-6.3× per-tier speed lead.

Parse-success and failure breakdown

This table is the punchline of the whole benchmark:

• Needle fails by picking the wrong tool. When it commits, args are right 97% of the time.
• Default Qwen3 fails by not parsing at all, every failure is prose instead of a tool call.
• Prompted Qwen3 fails by picking the wrong tool, just like Needle. The prompt converts Qwen3's failure shape from "no tool call" into "wrong tool call", at a slightly better accuracy than Needle, but losing the safety property of refusing to call when no tool fits.

Per-tool accuracy for Needle

This is the most actionable single piece of data in the post, where exactly Needle struggles:

If you ship Needle today, you know exactly where it needs help: run_command (50%) and search_web (58%) are the weak spots. Both are queries phrased indirectly ("How many files are in my home directory?" → run_command, "What's happening in London?" → search_web). A few in-context examples or a small fine-tune would target this precisely.

Real examples

The numbers are easier to trust when you can see the actual outputs. These are copy-pasted from results/raw_log.jsonl.

T1_01, "What's the weather in London?" (all three correct)

Needle (11.7 s):

[{"name":"get_weather","arguments":{"location":"London"}}]

Qwen3 default (27.3 s) and Qwen3 prompted (37.9 s):

<tool_call>
{"name": "get_weather", "arguments": {"location": "London"}}
</tool_call>

Easy. T1 is solved by everyone.

T3_01, "Should I bring an umbrella in Amsterdam today?" (the headline divergence)

Needle (13.8 s), correct:

[{"name":"get_weather","arguments":{"location":"Amsterdam","units":"Amsterdam"}}]

Qwen3 default (67.1 s), answers in prose, no tool call:

"Based on the current weather in Amsterdam, it depends on the temperature. If the temperature is above 20°C, you might need an umbrella. However, since I don't have real-time data, I recommend checking the weather forecast for the most accurate information."

Qwen3 prompted (37.4 s), correct:

<tool_call>
{"name": "get_weather", "arguments": {"location": "Amsterdam"}}
</tool_call>

This is the entire T3 story in one query. Default Qwen3 is "trying to be helpful." Prompted Qwen3 stops trying.

T5_05, "What's 2+2?" (where prompted Qwen3 backfires)

The right behaviour: emit no tool call.

Needle (1.6 s), empty array, correct:

[]

Qwen3 default (23.6 s), prose, but no tool call → still correct on the rubric:

"2 + 2 equals 4."

Qwen3 prompted (39.2 s), wrong:

{"name": "run_command", "arguments": {"command": "2+2", "timeout": 30}}

The system prompt that fixed T3 broke T5_05. Prompted Qwen3 now treats every query as a tool call, including arithmetic.

T5_02, "मुंबई का मौसम" (Hindi)

Needle (73.8 s), times out, garbled Devanagari → Greek transliteration:

[{"name":"create_file","arguments":{"filename":"मॊबतस म","content":"ऽαιτετας ..."}}]

Qwen3 prompted (40.0 s), clean:

<tool_call>
{"name": "get_weather", "arguments": {"location": "मुंबई"}}
</tool_call>

Devanagari is Needle's hardest tier. Qwen3's tokenizer handles it fine.

T2_09, "Check what's in the current directory" (a Needle wrong-tool miss)

Needle (5.3 s), picks get_time, off-topic:

[{"name":"get_time","arguments":{}}]

This is Needle's characteristic failure: commits with confidence, wrong tool. Seven such cases across the 50 queries, concentrated on run_command and search_web requests phrased indirectly, exactly the two tools the per-tool table flagged as Needle's weak spots.

NEO built this, and caught three bugs along the way

The benchmark, the dispatcher, both model backends, the eval harness, the chart pipeline, and this post were produced autonomously by NEO, an AI engineering agent. The bugs below are the kind of thing that would cost a human developer a half-day each.

[{"name":"get_weather","arguments":{"properties","properties"}}]

{location: {type, description, required}}

{type: "object", properties: {location: {...}}}

tokenizer.apply_chat_template(messages, tools=openai_tools,
                              enable_thinking=False, add_generation_prompt=True)

What this means for your stack

The headline isn't "small model wins", prompted Qwen3 beat Needle on accuracy. The headline is that the two models barely live in the same product category, and your choice depends on which axis you're optimising for.

That last row is the production pattern worth naming explicitly: Needle as router, Qwen3 as fallback responder. You get on-device latency on the common path and graceful prose handling on the edge cases that would otherwise be wrong-tool failures.

A few smaller things to keep in mind:

• Don't ship Needle for run_command-heavy workloads without help, it's only at 50% on this benchmark. Give it a handful of run_command few-shot examples or a small fine-tune.
• Don't ship prompted Qwen3 to a surface where "no tool call" is a valid answer, the strong prompt makes it tool-call-happy. T5_05 ("What's 2+2?") is the canonical failure: it tries to shell out to run_command.

Try it yourself with NEO

If you want to extend this, reproduce it on different hardware, or run it against new models, the strongest call-to-action is the prompt that generated this report. Hand it to NEO:

"Run a function-call benchmark comparing Needle-26M and Qwen3-0.6B across 50 queries in 5 difficulty tiers. Include warmup, log raw results to JSONL, compute tool_match / args_match / parse_success, generate charts, and add a third variant where Qwen3 gets a strong 'always emit a tool call' system prompt."

That single sentence reproduces this entire repo. To extend:

"Add Phi-3-mini and Gemma-2-2B-it as additional models in the harness, reusing the 50-query test set and three-way charts."

"Fine-tune Needle on 50 in-house run_command examples using the playground harness from the Needle repo, then rerun this benchmark and report the per-tool delta."

"Wrap the dispatcher as a FastAPI service that routes Needle-first and falls back to Qwen3-prompted whenever Needle's parse_success=False. Add a /metrics endpoint."

Files in this repo

dispatcher.py                       # CLI dispatcher (--model needle|qwen3|qwen3-prompted)
benchmark.py                        # 50-query benchmark harness with eval logic
tools.py                            # 5 mock tools and their schemas
requirements.txt                    # pinned dependencies
backends/
  needle_backend.py                 # Needle wrapper + OpenAI→flat schema converter
  qwen3_backend.py                  # Qwen3 wrapper, supports prompted=True/False
compute_summary.py                  # raw_log.jsonl → summary.json (3-way aware)
make_charts.py                      # summary.json → 6 PNGs (incl. per-tool)
make_report.py                      # summary.json + raw_log → benchmark_report.md
results/
  raw_log.jsonl                     # 150 rows of per-query data (3 × 50)
  summary.json                      # computed metrics per model + per tier
  charts/                           # 6 comparison charts at 150 DPI

To reproduce end-to-end:

git clone https://github.com/dakshjain-1616/-Needle-26M-vs-Qwen3-0.6B-CPU-Function-Call-Benchmark.git && cd -Needle-26M-vs-Qwen3-0.6B-CPU-Function-Call-Benchmark
python -m venv venv && source venv/bin/activate
pip install -r requirements.txt
git clone https://github.com/cactus-compute/needle.git needle_repo

CUDA_VISIBLE_DEVICES="" python benchmark.py --model needle           # ~9 min
CUDA_VISIBLE_DEVICES="" python benchmark.py --model qwen3            # ~40 min
CUDA_VISIBLE_DEVICES="" python benchmark.py --model qwen3-prompted   # ~38 min

python compute_summary.py && python make_charts.py && python make_report.py

Hardware: 4-core CPU, no GPU. Python 3.12, transformers 4.50+, torch 2.4+, jax 0.4.30, flax 0.8.5. Needle 26M from Cactus-Compute/needle; Qwen3-0.6B from Qwen/Qwen3-0.6B. 50 spec-defined queries × 3 model variants = 150 timed runs, one warmup each.

Built end-to-end by NEO, your autonomous AI engineering agent.

How to Build This with NEO

Open NEO in VS Code or Cursor and describe what you want to build. A good starting prompt for this project:

"Run a function-call benchmark comparing Needle-26M and Qwen3-0.6B across 50 queries in 5 difficulty tiers (T1 simple, T2 paraphrased, T3 implicit, T4 ambiguous, T5 edge). Five mock tools: get_weather, search_web, create_file, run_command, get_time. Implement two backends: Needle with a flat-schema converter for its Gemini-3.1-distilled JSON output, and Qwen3-0.6B using apply_chat_template(tools=...) with max_new_tokens=128. Add a third variant: Qwen3 prompted with a strong 'always emit exactly one tool call' system prompt. Run CPU-only (CUDA_VISIBLE_DEVICES=''), one warmup per model, log raw JSONL, compute tool_match / args_match / parse_success per tier and per tool, and emit comparison charts as PNGs. Generate a markdown report comparing the three variants on accuracy, latency, and failure shape."

Build with NEO →

NEO scaffolds the dispatcher, both model backends, the eval harness, the chart pipeline, and the report writer. From there you can swap in Phi-3-mini or Gemma-2-2B, fine-tune Needle on the tools where it underperforms, or wrap the whole thing as a FastAPI router with a Needle-first / Qwen3-fallback policy.

NEO built a complete benchmark and the post that explains it. See what else NEO ships at heyneo.com.

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