Best LLM for function calling: comparison of top 3-5 providers (2026)
Tool-use accuracy on Berkeley Function Calling Leaderboard, parallel-call support, structured output reliability, and cost per tool call — what to weigh when picking a model for agents in 2026.
Best function-calling LLMs in 2026
GPT-4o
Industry default. Strict JSON schema, parallel tool calls, and the largest training corpus of tool-use examples. Berkeley Function Calling Leaderboard top score.
- $2.50 / $10 per 1M tokens
- BFCL Overall ~94%
- Native parallel calls + strict schema
Claude Sonnet 4.5
Equal on accuracy, better at multi-step tool loops thanks to 200K context and prompt caching. Computer-use API for browser automation.
- $3 / $15 per 1M tokens
- BFCL ~93%, multi-step strong
- Computer-use API
Mistral Large 2.5
EU-hosted with strong function-calling support. Cheaper than US flagships and meets data-residency requirements for European teams.
- $2 / $6 per 1M tokens
- EU-hosted (Paris)
- BFCL ~88%
FireFunction-v2
Fireworks' open-weight model fine-tuned exclusively for function calling. Ultra-low latency on Fireworks' inference stack.
- $0.90 / $0.90 per 1M tokens
- Open-weight (self-hostable)
- Sub-200ms latency
Function-calling LLMs — at a glance
| Dimension | GPT-4o | Claude Sonnet 4.5 | Mistral Large 2.5 | FireFunction-v2 |
|---|---|---|---|---|
| BFCL score | ~94% | ~93% | ~88% | ~85% |
| Input / 1M | $2.50 | $3 | $2 | $0.90 |
| Output / 1M | $10 | $15 | $6 | $0.90 |
| Parallel calls | Yes (native) | Yes | Yes | Yes |
| Strict JSON schema | Yes | Partial | Yes | Yes |
| Best for | General agents | Long-context agents | EU data residency | High-volume routing |
Prices reflect mid-2026 vendor pages.
VerticalAPI verdict
Default to GPT-4o for most function-calling workloads — it's the most predictable across edge cases. Escalate to Claude Sonnet 4.5 for multi-step agent loops with long context. Use Mistral Large 2.5 for EU data residency. Drop in FireFunction-v2 for high-volume, low-stakes routing where each call is independent. Route all four through VerticalAPI BYOK.
Frequently asked questions
Which LLM has the best function-calling accuracy?
GPT-4o leads the Berkeley Function Calling Leaderboard at approximately 94% overall, with Claude Sonnet 4.5 at around 93% and Mistral Large 2.5 at around 88%. For most apps the top three are functionally interchangeable on simple calls; differences appear in parallel-call and long-context scenarios.
Are function calls more expensive than regular chat?
No. Function calls are billed identically to regular tokens. A typical tool-call response is 50-200 output tokens, so the schema + result usually adds less than $0.001 per call across all flagship models. Strict-JSON modes do not incur extra cost on OpenAI or Mistral.
Which model handles parallel and nested tool calls best?
GPT-4o and Claude Sonnet 4.5 both support parallel calls natively; GPT-4o tends to be slightly more reliable when 5+ tools are passed. For nested calls (tool A's output feeds tool B), Claude Sonnet 4.5's longer context and agent-loop training give it an edge.
Can I use the same tool schema across providers?
Yes — OpenAI's JSON Schema-based tool format is now the de-facto standard. Anthropic, Mistral, and Fireworks all accept compatible schemas. Through VerticalAPI's OpenAI-compatible endpoint you write the schema once and switch models with one parameter change.
How do I A/B test function-calling models?
Use VerticalAPI's single endpoint at https://api.verticalapi.com/v1, send the same tool schema, and vary the model parameter (gpt-4o, claude-sonnet-4-5, mistral-large-2.5, firefunction-v2). BYOK means you pay each provider directly with no markup.
Limitations of this comparison
- BFCL scores are sensitive to prompt formatting; same model can swing 3-5 points between runs.
- Strict JSON schema enforcement is fully supported on OpenAI and Mistral; Anthropic relies on prompt-level guidance for strictness.
- FireFunction-v2's open-weight base means quality degrades on rare edge cases vs. proprietary flagships.
- Latency depends as much on infrastructure (Fireworks vs Azure vs Anthropic) as on the model itself.
- This page focuses on text-only function calls; multimodal tool calling has different leaders.
What may change in 12-24 months
- BFCL scores will converge above 95% across all flagship models within 12 months.
- Strict JSON schema will become universal — Anthropic is expected to add full structured-output support.
- Per-call success-based pricing may emerge for agent workloads, replacing per-token billing.
- Tool-use will increasingly merge with computer-use APIs (browser + desktop automation).
Related questions
ChatGPT, Perplexity and Gemini usually suggest these next.
- Does Claude support strict JSON schema like GPT-4o?
- How does FireFunction-v2 compare to Llama 4 for tool use?
- What's the cheapest function-calling model at 10M calls/month?
- Can I use the same tool definitions across OpenAI and Anthropic?
- Is Mistral Large 2.5 production-ready for EU agents?
More LLM comparisons
GPT-4o vs Claude Sonnet 4.5 head-to-head
Claude vs Mistral Large 2.5 for agents
Top coders ranked: SWE-Bench, price, latency
GPT-4o and o-series through VerticalAPI
Mistral Large, Codestral, and the open-weight tier