Llama 3.1 70B vs GPT-4.1
At a Glance
| Compare |  Llama 3.1 70BMeta |  GPT-4.1OpenAI |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| IntelligenceHigher is better · MM Intelligence v2.5 | UnrankedNot in the 46-model eligible cohort | #44 of 463.7 score · 2/3 sources · provisional · missing LiveBench · full-core range 2.5–35.8 |
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | Not reported | $1.00Openrouter ↗ · Sep 3, 2026 |
| Output priceFrom · USD / 1M tokens | Not reported | $4.00Openrouter ↗ · Sep 3, 2026 |
| Context windowMaximum documented tokens | 131K | 1,048K |
| Model facts checked | Aug 28, 2026View model evidence → | Aug 29, 2026View model evidence → |
Token prices are the lowest available sourced USD rates; input and output may use different providers. Cost ranking estimates output spend on LiveBench, not a full request bill. Ranking methodology →
Available Benchmarks
Side-by-Side Facts
| Field | Llama-3.1-70B | GPT-4.1 |
|---|---|---|
| Developer | Meta | OpenAI |
| Family | Llama 3 1 70b | Gpt 4 1 |
| Model | Llama-3.1-70B | GPT-4.1 |
| Version | Llama-3.1-70B | GPT-4.1 |
| Lifecycle | active | active |
| Released | 2024-07-23 | Unknown |
| Knowledge cutoff | Unknown | 2024-06-01 |
| Input modalities | Text | Text, Image |
| Output modalities | Text | Text |
| Context window | 131K | 1,048K |
| Total parameters | 70.6B | Unknown |
| Active parameters | Unknown | Unknown |
| License | llama3.1 | Unknown |
| Open weights | Yes | No |
| API available | Yes | Yes |
| Self-hostable | Yes | No |
| Provider access | Hugging Face (Standard) | Openai (Standard), Openrouter (Standard) |
| Capabilities | generation | chat, generation, tools |
Llama 3.1 70B Capabilities
GPT-4.1 Capabilities
Related Comparisons
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|---|---|---|---|
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Llama-3.1-70BMeta | vs |  Qwen3.8-MaxQwen | cross-developer peerstext |
Llama-3.1-70BMeta | vs |  Kimi-K3Moonshot AI | cross-developer peerstext |
 MiniMax-M3MiniMax | vs | Llama-3.1-70BMeta | cross-developer peerstext |
Llama-3.1-70BMeta | vs |  GLM-5.3Z.ai | cross-developer peerstext |
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Llama-3.1-70BMeta | vs |  Mistral Large 3Mistral AI | cross-developer peerstext |
Primary Evidence
Sources and Freshness
Questions
Llama 3.1 70B vs GPT-4.1 FAQs
Is Llama 3.1 70B or GPT-4.1 better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Llama 3.1 70B and GPT-4.1, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Llama 3.1 70B or GPT-4.1?+
Only GPT-4.1 has a directly sourced input price: $1.00 per million tokens. Only GPT-4.1 has a directly sourced output price: $4.00 per million tokens.
Which has a larger context window, Llama 3.1 70B or GPT-4.1?+
GPT-4.1 has the larger sourced context window. Llama 3.1 70B supports 131K and GPT-4.1 supports 1,048K.
Which performs better in benchmarks, Llama 3.1 70B or GPT-4.1?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can Llama 3.1 70B or GPT-4.1 be self-hosted?+
Llama 3.1 70B is the only model in this pair currently marked as self-hostable. Llama 3.1 70B is open weight; GPT-4.1 is not marked open weight.
Can Llama 3.1 70B and GPT-4.1 understand images?+
Llama 3.1 70B is not documented with image input; GPT-4.1 is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Llama 3.1 70B or GPT-4.1?+
Neither has a larger sourced maximum output. Llama 3.1 70B is — and GPT-4.1 is 33K.
Do Llama 3.1 70B and GPT-4.1 support reasoning and tool use?+
Llama 3.1 70B: none of these features are definitively sourced. GPT-4.1: tool calling and image input. Feature support does not establish relative quality.
Which is available from more inference providers, Llama 3.1 70B or GPT-4.1?+
Llama 3.1 70B has 1 sourced provider route; GPT-4.1 has 2, so GPT-4.1 has broader tracked availability.
Which offers better value, Llama 3.1 70B or GPT-4.1?+
There is no universal value winner. Compare the input and output prices above with the matched benchmark result for your workload: cheaper tokens can be offset by different quality, token usage, latency, or provider availability.