Llama 5 vs GPT-5.4 vs Claude Opus 4.6 (April 2026)
Llama 5 vs GPT-5.4 vs Claude Opus 4.6
Llama 5 launched April 8, 2026. Here’s how Meta’s new open-weight flagship compares to the two closed frontier leaders.
Last verified: April 10, 2026
At a Glance
| Feature | Llama 5 | GPT-5.4 | Claude Opus 4.6 |
|---|---|---|---|
| By | Meta | OpenAI | Anthropic |
| Released | April 8, 2026 | March 5, 2026 | February 4, 2026 |
| Parameters | 600B+ (MoE) | Undisclosed | Undisclosed |
| Context | 5M tokens | 256K | 200K (1M exp.) |
| Open weights | ✅ Yes | ❌ No | ❌ No |
| API Input | ~$3-5/M (hosted) | $15/M | $15/M |
| API Output | ~$6-9/M (hosted) | $60/M | $75/M |
| Best for | Self-hosting, agents | Reasoning | Coding, agent teams |
Llama 5 Strengths
- Open weights — Run it anywhere, no vendor lock-in, no rate limits
- 5M context — Longest of any frontier model; ingest entire codebases
- 3–5x cheaper on hosted APIs, free if you self-host
- Recursive self-improvement — Novel architecture for System 2 reasoning
- Native agentic training — Tool use baked into the base model, not bolted on
Weaknesses: Still slightly behind Claude Opus 4.6 on SWE-bench (74% vs 80.8%) and behind GPT-5.4 Thinking on hardest math/reasoning. Day-one tooling is thinner than closed competitors.
GPT-5.4 Strengths
- Reasoning leader — GPT-5.4 Thinking still tops hardest reasoning benchmarks (AIME, GPQA)
- Three tiers — Standard, Thinking, Pro for different workload costs
- Largest ecosystem — ChatGPT, Copilot, Azure, plus every dev tool integration
- Native multimodal — Image, audio, and video input in one API
- Codex — Strong autonomous coding agent
Weaknesses: Most expensive on output tokens. No open weights. 256K context is now small by comparison.
Claude Opus 4.6 Strengths
- Coding king — 80.8% on SWE-bench Verified, still the top autonomous coding model
- Claude Code — Best-in-class terminal coding agent
- Agent teams — Multiple Claude instances collaborating via Cowork
- Safety & alignment — Strongest of the three
- Writing quality — Preferred by many for long-form output
Weaknesses: Smallest default context (200K). Most expensive output pricing ($75/M). Subscription access ended for third-party tools in April 2026 — you now pay API rates.
Benchmark Snapshot (April 2026)
| Benchmark | Llama 5 | GPT-5.4 | Claude Opus 4.6 |
|---|---|---|---|
| MMLU-Pro | ~87% | 85% | 86% |
| SWE-bench Verified | ~74% | 74.9% | 80.8% |
| AIME 2025 | ~88% | 93% (Thinking) | 87% |
| GPQA Diamond | ~84% | 87% | 85% |
| LiveCodeBench | ~68% | 72% | 78% |
Llama 5 figures are from Meta’s day-one announcement and early independent tests; final numbers may shift as third parties verify.
Which Should You Pick?
| Use Case | Pick |
|---|---|
| Self-hosted frontier AI | Llama 5 |
| Longest context | Llama 5 (5M) |
| Autonomous coding | Claude Opus 4.6 |
| Hardest reasoning/math | GPT-5.4 Thinking |
| Lowest cost at scale | Llama 5 (self-host) |
| Enterprise with compliance | Claude Opus 4.6 or Llama 5 (on-prem) |
| Fastest integration | GPT-5.4 |
| Agent teams | Claude Opus 4.6 |
The Big Picture
Llama 5 changes the math. For the first time, an open-weight model credibly competes with closed frontier. Teams that can run 600B-parameter inference now have a genuine alternative to Anthropic and OpenAI — and for agentic workloads with long context, Llama 5 may actually be the best choice.
Closed models still lead on specific axes (Claude for code, GPT for hardest reasoning), but the “closed frontier is always better” era is over.
Last verified: April 10, 2026