OpenAI vs Claude vs Hermes for AI agents
Every AI agent project I scope starts with the same fork: which model do we put behind the reasoning layer? OpenAI, Anthropic, or one of the open models like Hermes? There is no universal winner — each one is best at something specific. Here is the matrix I actually use when picking.
The 60-second version
- Claude Opus 4.7 — best default for agents with tool use, long context (1M tokens), and complex reasoning. Slower and pricier per token, but you write fewer prompts.
- GPT-5 / GPT-5 Mini — best for latency-critical and high-volume agents. Cheapest path for chat-style assistants and structured outputs.
- Hermes 4 / open models — best for EU/UA data residency, full ownership, regulated industries. Higher engineering cost up front, near-zero token cost long-term.
Round 1: long context
How much you can stuff into one prompt without losing track.
- Claude Opus 4.7 — 1M tokens, with the strongest retrieval-in-context I have measured. Reads 600-page PDFs and answers questions accurately at depth.
- GPT-5 — 400K context, very fast on retrieval, but starts to "skim" past 250K. Pair it with proper RAG for anything larger.
- Hermes 4 (70B) — 128K context on self-hosted deployments. Enough for 95% of real workloads, especially with RAG.
Rule of thumb: above ~200K tokens of actively reasoned context, Claude is the only model I trust without a retrieval layer. Below that, all three are viable.
Round 2: tool use and function calling
This is where agents live or die. The model has to decide which function to call, when, with what arguments, and how to react to the result.
- Claude — best multi-tool reasoning. Picks the right tool out of 20+ candidates with ~96% accuracy in my evals. Strong at chaining 5-7 tool calls without losing the original goal.
- GPT-5 — slightly behind Claude on multi-tool, but faster. ~93% tool selection accuracy, lower per-call latency (300-600ms). Great for high-throughput agents.
- Hermes 4 — needs more careful prompting and a strict JSON schema, but with a fine-tune it gets to ~91% on a narrowed tool set.
Round 3: latency
Time to first token, then token throughput.
- GPT-5 Mini— 200-400ms TTFT, 90-130 tokens/sec. Best for voice agents and Telegram bots where a reply in > 2 sec feels dead.
- Claude Haiku 4.5 — 300-500ms TTFT, 70-110 tokens/sec. Comparable, with much better reasoning at the same speed tier.
- Claude Opus 4.7 — 600-1200ms TTFT. Not for real-time chat, perfect for back-office agents and analysis.
- Hermes 4 (self-hosted) — depends entirely on your GPU. On 2× H100 you get ~250ms TTFT and 60 tokens/sec.
Round 4: price
Real production numbers — input + output tokens combined for an average agent turn.
- Claude Opus 4.7 — ~$15 / 1M input, $75 / 1M output. Heavy but you do less retry work.
- Claude Sonnet 4.5 — ~$3 / 1M input, $15 / 1M output. The default I use for 80% of projects.
- GPT-5 — ~$5 / 1M input, $20 / 1M output. Competitive on Sonnet's tier.
- GPT-5 Mini — ~$0.40 / 1M input, $1.60 / 1M output. Cheapest for chat-style high-volume.
- Hermes 4 self-hosted — $0 per token, but $400-1,200 per month for GPU. Breaks even at ~50M tokens/month.
Round 5: EU / UA compliance and data residency
For Ukrainian and EU clients in fintech, healthcare, and government — this round is often decisive.
- Claude via AWS Bedrock (EU region) — GDPR-compliant, data stays in Frankfurt or Paris. My default for EU clients.
- OpenAI via Azure OpenAI (EU) — also GDPR-compliant, enterprise-only. Slightly higher friction in setup.
- Hermes 4 on-prem or in EU VPS — full data control, no third party touches the prompt. Only path that passes for classified Ukrainian state contracts and most banks.
The decision matrix I actually use
When a client says...
- "Telegram bot, < 2s reply, 10K msgs/day" → GPT-5 Mini or Claude Haiku 4.5.
- "Multi-agent system with 5+ specialized agents" → Claude Sonnet 4.5 for the orchestrator, Haiku for the specialists.
- "Read 100-page contracts and find anomalies" → Claude Opus 4.7. Nothing else comes close.
- "Bank, fintech, ministry — data stays in Ukraine" → Hermes 4 self-hosted on a Ukrainian datacenter. No exceptions.
- "Voice agent answering the phone" → GPT-5 Mini for speed, or Claude Haiku 4.5 if reasoning matters more than 100ms.
- "Cheapest possible MVP that still works" → Claude Sonnet 4.5. Best quality-per-dollar in 2026.
A few things I no longer recommend
- GPT-4o / GPT-4 Turbo — outdated for agents. GPT-5 Mini outperforms GPT-4o on tool use at one-fifth the cost.
- Llama 3.x base for agents — Hermes 4 is the fine-tune you actually want. Base Llama hallucinates tool calls.
- Gemini for tool use — fast, but tool-calling accuracy still trails Claude and GPT-5 in my evals. Fine for single-turn summarization.
What I would NOT mix in one stack
Tempting trap: "let's use Claude for reasoning and GPT-5 for embeddings". Cross-vendor latency, two billing accounts, two SDK styles, two failure modes. Pick one provider as the spine, add a second only when there is a measured reason — e.g., self-hosted Hermes for sensitive PII, Claude for the rest.
Recommendation per use-case
E-commerce / SaaS / agency — Claude Sonnet 4.5 as the default. Best ratio of quality, speed, and price. Switch to Opus 4.7 only if you measure that Sonnet is failing on your specific edge cases.
High-volume support / Telegram / voice — GPT-5 Mini. Cheap enough to retry, fast enough not to feel like a bot. Watchful eye on the OpenAI bill — easy to 10× overnight.
Regulated industries (fintech, healthcare, gov) — Hermes 4 self-hosted, with Claude via Bedrock as a fallback for non-sensitive flows. Costs more in engineering, saves you from any data-leak narrative.
Not sure which one fits your case?
I do not sell models. I help you pick the one that matches your constraints. 30-minute call — I listen to the process, the volume, the compliance bar, and tell you which model goes where. Honest, no upsell.