R1 0528 vs GPT-5.4 for Long Context
Winner: GPT-5.4. In our testing both R1 0528 and GPT-5.4 score 5/5 on Long Context and tie for 1st (rank 1 of 52). Practically, GPT-5.4 is the stronger choice because it offers a vastly larger context_window (1,050,000 tokens vs R1 0528's 163,840) and an explicit max_output_tokens of 128,000, which directly benefits tasks that exceed R1 0528's 160k-ish capacity. R1 0528 remains the better value option for many long-context workflows due to much lower input/output costs (input $0.50 vs $2.50 per mTok; output $2.15 vs $15 per mTok) and stronger tool_calling (5 vs 4), but quirks (reasoning tokens consuming output budget; empty responses on structured_output unless configured) make GPT-5.4 the definitive pick when raw capacity, long outputs, and structured-output reliability matter most.
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
What Long Context demands: retrieval accuracy at 30K+ tokens (our task definition). Key capabilities: raw context capacity, ability to produce long outputs, faithfulness to source material across far-apart context locations, stable structured-output behavior for large documents, tool calling for retrieval and indexing workflows, and safety calibration when deciding what to reveal. Primary signal: both models score 5/5 on our long_context test and tie for rank 1 of 52 in our 12-test suite. Supporting evidence from other proxies: GPT-5.4 has a context_window of 1,050,000 tokens and max_output_tokens 128,000, improving headroom for >160k-token retrieval and long-form synthesis. R1 0528 has a 163,840-token window and also scores 5/5 on long_context, but its quirks — uses reasoning tokens that consume output budget, requires high max_completion_tokens, and returns empty responses on structured_output by default — can complicate long-run structured workflows. On related benchmarks, both models score 5/5 for faithfulness and persona_consistency; GPT-5.4 scores higher on structured_output (5 vs R1's 4) and safety_calibration (5 vs 4), while R1 0528 leads on tool_calling (5 vs 4) and is materially cheaper per mTok. Use these trade-offs to match the model to your workflow.
Practical Examples
When to prefer GPT-5.4: 1) Ingest and synthesize a million-token codebase or corpus — GPT-5.4's context_window 1,050,000 vs R1 0528's 163,840 tokens gives clear headroom. 2) Produce very long, structured JSON outputs for downstream tools — GPT-5.4 scores 5 on structured_output vs R1 0528's 4 and avoids R1's 'empty on structured_output' quirk. 3) Safety-sensitive extraction from long documents — GPT-5.4 scores 5 on safety_calibration vs 4 for R1 0528. When to prefer R1 0528: 1) Cost-sensitive, repeated long-context queries where you stay within ~160k tokens — R1 0528 input $0.50 and output $2.15 per mTok versus GPT-5.4's $2.50/$15 per mTok. 2) Agentic flows relying on tool selection and sequencing — R1 0528 scores 5 on tool_calling vs GPT-5.4's 4. 3) Teams wanting open reasoning-token behavior and a cheaper platform for heavy long-context experimentation, accepting that you must configure high max_completion_tokens and work around its structured-output quirk.
Bottom Line
For Long Context, choose R1 0528 if you need a far cheaper, capable long-context model and your use stays within ~163,840-token windows or you rely heavily on tool calling and can tolerate R1's structured-output quirks. Choose GPT-5.4 if you need maximum raw capacity and large structured outputs (1,050,000-token window and 128,000 max output tokens), stronger structured-output reliability, and tighter safety calibration—even at ~7x higher output cost ($15 vs $2.15 per mTok).
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.