Llama 5 vs Gemini 3.1 Pro (April 2026 Comparison)
Llama 5 vs Gemini 3.1 Pro
Two of the most capable AI models in April 2026, with very different strengths. Here’s how to pick.
Last verified: April 10, 2026
Quick Comparison
| Feature | Llama 5 | Gemini 3.1 Pro |
|---|---|---|
| By | Meta | Google DeepMind |
| Released | April 8, 2026 | February 19, 2026 |
| Parameters | 600B+ MoE | Undisclosed |
| Context | 5M tokens | 2M tokens |
| Open weights | ✅ Yes | ❌ No |
| API Input | ~$3-5/M (hosted) | ~$1.25-2.50/M |
| API Output | ~$6-9/M (hosted) | ~$10-15/M |
| Best benchmark | Long context, agents | MMLU-Pro (94.1%) |
Llama 5 Strengths
- Open weights — Self-host, fine-tune, run offline
- 5M token context — 2.5x larger than Gemini 3.1 Pro
- Strong agentic training — Native tool use and planning
- Recursive self-improvement — Novel architecture
- Day-one ecosystem support — Ollama, vLLM, Bedrock, Together, Fireworks, Groq
Weaknesses: Behind Gemini 3.1 Pro on MMLU-Pro and on video understanding. Larger to serve at full precision. No native connection to Google’s search/real-time data.
Gemini 3.1 Pro Strengths
- MMLU-Pro leader — 94.1% as of April 2026, highest of any frontier model
- Best video understanding — Google’s multimodal lead remains strong
- Native Google integration — Direct access to Google Search, Maps, YouTube data grounding
- Gemini app ecosystem — 750M+ users, mature product surface
- Competitive pricing — Lower input token cost than Llama 5 hosted providers
- Gemini CLI / AI Studio — Free tier for developers
Weaknesses: Closed weights — can’t self-host. Smaller context than Llama 5 (2M vs 5M). Behind on autonomous coding benchmarks (Claude Opus 4.6 and even Llama 5 trail-close here).
Benchmark Snapshot
| Benchmark | Llama 5 | Gemini 3.1 Pro |
|---|---|---|
| MMLU-Pro | ~87% | 94.1% |
| SWE-bench Verified | ~74% | ~72% |
| AIME 2025 | ~88% | ~89% |
| GPQA Diamond | ~84% | ~85% |
| Video-MME | ~70% | ~82% |
| Long-Bench | ~92% | ~88% |
Cost Comparison (per 1M tokens)
| Provider | Input | Output |
|---|---|---|
| Gemini 3.1 Pro (Google API) | $1.25-2.50 | $10-15 |
| Llama 5 (Together) | ~$3.50 | ~$7 |
| Llama 5 (Fireworks) | ~$4 | ~$8 |
| Llama 5 (Groq) | ~$5 | ~$9 |
| Llama 5 (self-hosted) | Hardware only | Hardware only |
Winner on cost:
- Low input volume, high output: Llama 5 (lower output cost)
- High input volume (long context): Gemini 3.1 Pro (cheaper input tokens)
- Unpredictable high volume: Llama 5 self-hosted (no per-token cost)
Multimodal Comparison
| Modality | Llama 5 | Gemini 3.1 Pro |
|---|---|---|
| Text | ✅ Frontier | ✅ Frontier |
| Images | ✅ Strong | ✅ Strong |
| Video | ✅ Good | ✅ Best-in-class |
| Audio | ✅ Native | ✅ Native |
| Grounding (web/search) | ❌ No native | ✅ Google Search |
Which Should You Pick?
| Use Case | Pick |
|---|---|
| Highest general knowledge | Gemini 3.1 Pro |
| Longest context | Llama 5 (5M) |
| Video analysis | Gemini 3.1 Pro |
| Self-hosted frontier | Llama 5 |
| Fine-tuning on your data | Llama 5 |
| Google ecosystem apps | Gemini 3.1 Pro |
| Real-time web grounding | Gemini 3.1 Pro |
| Air-gapped deployment | Llama 5 |
| Autonomous coding | Llama 5 (still trails Claude Opus 4.6) |
| Agent workflows | Llama 5 |
The Strategic Angle
Google and Meta are playing different games:
- Google (Gemini): Premium closed-API frontier model, wrapped in search grounding, distributed through the Gemini app to 750M+ users. Best-in-class multimodal.
- Meta (Llama 5): Commoditize the model layer, capture value at the app and device layer (Meta AI, Ray-Ban Meta glasses). “Linux of AI” strategy.
Both strategies can win. For builders, the choice often comes down to where your data lives (Google Cloud → Gemini; anywhere else → Llama 5) and what modalities matter most (video → Gemini; long context → Llama 5).
Last verified: April 10, 2026