AI SOURCE NETWORKINDEPENDENT AI BAROMETER

PUBLIC BFCL CASES · LOCAL OLLAMA · EXPLORATORY

Both models chose the right tool.
Half the calls had exact arguments.

Five public BFCL examples compared Qwen 2.5 1.5B and Llama 3.2 3B on a single local Windows machine. In all ten responses, the selected function matched the expected function. Only five calls also matched the exact typed arguments: Qwen passed 3/5; Llama passed 2/5.

10 OUTPUTS · 5 PUBLIC EXAMPLES · NOT A RANKING

FUNCTION CHOICE VS COMPLETE CALL

Observed outcomes

EXPLORATORY · NOT PRODUCTION

Choosing the right tool is only one part of a usable tool call. The argument types and values must also be correct. The first column below measures the function choice; the second requires a full oracle match.

Local model buildExpected function selectedExact function + argumentsVerified failuresExecution errors
Qwen 2.5 1.5B5 / 53 / 520
Llama 3.2 3B5 / 52 / 530
All responses10 / 105 / 1050

The 95% Wilson intervals for exact calls are wide and overlap (Qwen 23–88%; Llama 12–77%). The one-example difference does not support a model ranking. Function-choice accuracy is 5/5 for both models here, also too small to generalize.

Where arguments failed

Public taskQwen 2.5 1.5BLlama 3.2 3B
Currency conversion · current rateSelected correct tool; left live_conversion falseSelected correct tool; emitted amount and boolean as strings
Used guitar priceSelected correct tool; omitted “Gibson” from the modelExact call
Weight on MarsExact callSelected correct tool; emitted weight as a string
Building floor analysisExact callSelected correct tool; emitted floor numbers as strings
Average Apple closing priceExact callExact call

Wall-clock request medians were 17.0 s for Qwen and 18.5 s for Llama on this host. They include local model/runtime conditions and are not general speed estimates. No selected tool was executed, so this study measures call selection and arguments—not the freshness, correctness or usefulness of any downstream tool result.

INDEPENDENT EXTENSION · 24 OCT 2026 · 12 NEW TASKS

Function choice was strong; exact calls were not.

24 LOCAL OUTPUTS · NOT A RANKING

A deterministic, disjoint 12-task extension of the five-case pilot above tested the same installed model builds on new public BFCL V4 examples. Qwen selected the expected function in 11/12 responses and passed the exact function-plus-arguments check in 8/12. Llama selected the expected function in 12/12 and passed the exact check in 2/12.

Local model buildExpected function selectedExact function + typed argumentsVerified failuresExecution errors
Qwen 2.5 1.5B11 / 128 / 1240
Llama 3.2 3B12 / 122 / 12100
All responses23 / 2410 / 24140

The exact-call 95% Wilson intervals are wide and overlap (Qwen 39–86%; Llama 5–45%). The task sample is small, public and potentially present in training data. These results describe only these model builds and prompts; they do not establish a general winner, production ranking or customer utility.

All 10 Llama failures were invalid argument schemas. Qwen's three argument mismatches followed the correct function selection; its fourth failure was a response without exactly one tool call. This small sample shows why tool selection alone does not establish a usable tool call.

No tool was executed. We tested function selection and typed arguments, not whether a downstream service exists, returns fresh data or solves the user's task.

Source and selection
BFCL V4 multiple-function category, pinned to the source commit above. Twelve tasks were deterministically selected by a published hash rule from 67 eligible cases, excluding the five pilot tasks.
Protocol
One response per model-task pair; Ollama loopback; temperature 0; seed 42; 60-second request cap; 20-minute total cap. 24 local calls, zero provider calls and $0 provider spend. Electricity and hardware opportunity cost were not measured.
Model builds
Qwen 2.5 1.5B 65ec06548149b04c096a120e4a6da9d4017ea809c91734ea5631e89f96ddc57b; Llama 3.2 3B a80c4f17acd55265feec403c7aef86be0c25983ab279d83f3bcd3abbcb5b8b72.
Independent evidence
Plan SHA-256 0df8705c39854f68123162d101f2692290afeddb15f9598195cfd0ed6510468e; local run SHA-256 057f7d2b5c0d92a2dae26f497f91f80c4f57835fd171868fd08e5ff6cfce514c; public receipt SHA-256 1009ad7b6206b7fd80a5e7b8d59a4781ac12796d1797c45dc85f2c64668042a3. Inspect all 12 tasks, expected arguments, model calls and verification outcomes ↗.
Production status
Exploratory evidence only. It is excluded from the production measurement index, routing and rankings. It did not execute tools, write production data or change system authority.

Protocol, provenance and limits

Source
Berkeley Function Calling Leaderboard (BFCL), V4 multiple-function selection category, pinned to commit 58f57e9124ea981403792dd51e00a6577e621fae (Apache-2.0). Model exposure to these public examples during training is unknown.
Grading
Independent exact check of one expected function, schema-valid argument types and source-oracle values. The correct function and full-call result are shown separately.
Execution
Ollama loopback only; one response per model and example; temperature 0; fixed seed; 60-second request timeout; 15-minute total cap. Provider calls and spend: zero. Local electricity cost was not measured.
Source hashes
Case file aef168155ebd74b7ac2401198b201343bc7d16d7a3d7e0d4e6d8ee82c6969b2a; answer file 244e00ce9395df948bcafc7bee64e8f9c87ef70887587d83cae45b13699f3047. The original run receipt preserved two mistyped hashes; the pinned files and all five task mappings have now been reverified. Inspect the source hash correction and task map ↗.
Model builds
Qwen 2.5 1.5B 65ec06548149b04c096a120e4a6da9d4017ea809c91734ea5631e89f96ddc57b; Llama 3.2 3B a80c4f17acd55265feec403c7aef86be0c25983ab279d83f3bcd3abbcb5b8b72.
Evidence receipt
Inspect task-level calls, typed arguments, scores and integrity hash ↗. Receipt SHA-256: 1aeac6517005551fba535520a9cc129a4ba1c71486e73c28f5618b4488dd903b.
Production status
This exploratory local pilot is excluded from production measurements, rankings and routing. It made no production writes and changed no system authority.

← Return to the live AI observatory