R1 0528 vs GPT-5.4 for Chatbots
Winner: GPT-5.4. In our testing GPT-5.4 scores 5.00 on the Chatbots task vs R1 0528's 4.6667 (rank 1 of 52 vs rank 6 of 52). GPT-5.4's 5/5 safety_calibration and 5/5 structured_output give it clearer reliability for persona-safe, schema-driven conversational experiences. R1 0528 remains compelling for tool-heavy, cost-sensitive deployments because it scores 5/5 on tool_calling and is substantially cheaper per token, but overall GPT-5.4 is the better choice for general-purpose chatbots where safety and strict output formats matter.
deepseek
R1 0528
Benchmark Scores
External Benchmarks
Pricing
Input
$0.500/MTok
Output
$2.15/MTok
modelpicker.net
openai
GPT-5.4
Benchmark Scores
External Benchmarks
Pricing
Input
$2.50/MTok
Output
$15.00/MTok
modelpicker.net
Task Analysis
Chatbots require three core capabilities: persona_consistency (stay in character), safety_calibration (refuse or safely handle harmful requests), and multilingual parity. Our Chatbots task uses those three benchmarks. Score summary in our testing: persona_consistency — GPT-5.4 5 vs R1 0528 5 (tie); safety_calibration — GPT-5.4 5 vs R1 0528 4 (GPT-5.4 advantage); multilingual — GPT-5.4 5 vs R1 0528 5 (tie). Supporting proxies that matter for production chatbots: structured_output (JSON/schema adherence) is 5 for GPT-5.4 vs 4 for R1 0528, and tool_calling (API selection/args) is 4 for GPT-5.4 vs 5 for R1 0528. Also note engineering-relevant differences: GPT-5.4 supports a far larger context window (1,050,000 tokens) and high max output (128,000 tokens), while R1 0528 has 163,840 tokens. R1 0528 has a known quirk in our tests — it returns empty responses on structured_output and agentic_planning unless configured with very high completion token limits and handles reasoning via explicit reasoning tokens — a practical constraint for schema-first chatbot designs.
Practical Examples
- Safety-sensitive customer support: GPT-5.4 (safety_calibration 5 vs 4) — better at refusing or safely rephrasing harmful prompts and maintaining safe persona guardrails. 2) Schema-driven transactional bot (invoices, appointment JSON): GPT-5.4 (structured_output 5 vs 4) produces more reliable JSON and format-adherent replies; R1 0528 may return empty structured outputs unless you accommodate its quirks. 3) API-orchestration assistant: R1 0528 (tool_calling 5 vs GPT-5.4 4) is stronger at selecting and sequencing function calls and arguments in our tests. 4) High-volume consumer chat: R1 0528 is far cheaper (input $0.50/mTok, output $2.15/mTok) vs GPT-5.4 (input $2.50/mTok, output $15.00/mTok), making R1 the cost-efficient choice when safety/strict schema are less critical. 5) Long-session, multi-file concierge: GPT-5.4's 1,050,000-token window supports extremely long histories and large context attachments; both models score 5/5 on long-context proxies used elsewhere, but GPT-5.4's raw window is materially larger.
Bottom Line
For Chatbots, choose R1 0528 if you need the most cost-efficient model that excels at tool calling and API orchestration (and you can engineer around its structured-output quirks). Choose GPT-5.4 if you prioritize safety, strict structured outputs, and the largest context window — it wins our Chatbots task 5.00 vs 4.6667.
How We Test
We test every model against our 12-benchmark suite covering tool calling, agentic planning, creative problem solving, safety calibration, and more. Each test is scored 1–5 by an LLM judge. Read our full methodology.