Llama 3.1 8B vs Jev
At a Glance
| Compare |  Llama 3.1 8BMeta |  JevTypeSafe |
|---|---|---|
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | Not reported | $0.042TypeSafe ↗ · Sep 17, 2026 |
| Output priceFrom · USD / 1M tokens | Not reported | $0.000TypeSafe ↗ · Sep 17, 2026 |
| Context windowMaximum documented tokens | 131K | 64K |
| Model facts checked | Aug 28, 2026View model evidence → | Sep 17, 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-3.1-8B | Jev |
|---|---|---|
| Developer | Meta | TypeSafe |
| Family | Llama 3 1 8b | Jev |
| Model | Llama-3.1-8B | Jev |
| Version | Llama-3.1-8B | Jev |
| Lifecycle | active | active |
| Released | 2024-07-23 | 2026-09-15 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text | Text, Model-specific input |
| Output modalities | Text | Model-specific input |
| Context window | 131K | 64K |
| Total parameters | 8B | 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) | TypeSafe (Standard) |
| Capabilities | generation | calibrated-confidence, parallel-evaluation, structured_outputs, typed-decisions |
| Maximum Choice cardinality | Unknown | 255 options |
| Default request rate limit | Unknown | 1200 requests per minute |
| State plus longest question limit | Unknown | 32000 tokens |
| Combined state and questions limit | Unknown | 64000 tokens |
| Default token rate limit | Unknown | 250000 tokens per second |
Llama 3.1 8B Capabilities
Jev Capabilities
Related Comparisons
All text comparisons →| A | Pair | B | Context |
|---|---|---|---|
Llama-3.1-8BMeta | vs |  GPT-6 AstraOpenAI | cross-developer peerstext |
Claude Fable 5.1Anthropic | vs | Llama-3.1-8BMeta | cross-developer peerstext |
 Gemini 3.1 ProGoogle DeepMind | vs | Llama-3.1-8BMeta | cross-developer peerstext |
 DeepSeek-V4.1-FlashDeepSeek | vs | Llama-3.1-8BMeta | cross-developer peerstext |
Llama-3.1-8BMeta | vs | Grok 4.6xAI | cross-developer peerstext |
Llama-3.1-8BMeta | vs |  Qwen3.8-MaxQwen | cross-developer peerstext |
Llama-3.1-8BMeta | vs |  Kimi-K3Moonshot AI | cross-developer peerstext |
 MiniMax-M3MiniMax | vs | Llama-3.1-8BMeta | cross-developer peerstext |
Llama-3.1-8BMeta | vs |  GLM-5.3Z.ai | cross-developer peerstext |
Llama-3.1-8BMeta | vs |  Hy4 previewTencent | cross-developer peerstext |
Seed 2.1 ProByteDance Seed | vs | Llama-3.1-8BMeta | cross-developer peerstext |
Llama-3.1-8BMeta | vs |  Mistral Large 3Mistral AI | cross-developer peerstext |
Primary Evidence
Sources and Freshness
Questions
Llama 3.1 8B vs Jev FAQs
Is Llama 3.1 8B or Jev better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Llama 3.1 8B and Jev, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Llama 3.1 8B or Jev?+
Only Jev has a directly sourced input price: $0.042 per million tokens. Only Jev has a directly sourced output price: $0.000 per million tokens.
Which has a larger context window, Llama 3.1 8B or Jev?+
Llama 3.1 8B has the larger sourced context window. Llama 3.1 8B supports 131K and Jev supports 64K.
Which performs better in benchmarks, Llama 3.1 8B or Jev?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can Llama 3.1 8B or Jev be self-hosted?+
Llama 3.1 8B is the only model in this pair currently marked as self-hostable. Llama 3.1 8B is open weight; Jev is not marked open weight.
Can Llama 3.1 8B and Jev understand images?+
Llama 3.1 8B is not documented with image input; Jev is not documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Llama 3.1 8B or Jev?+
Neither has a larger sourced maximum output. Llama 3.1 8B is — and Jev is —.
Do Llama 3.1 8B and Jev support reasoning and tool use?+
Llama 3.1 8B: none of these features are definitively sourced. Jev: none of these features are definitively sourced. Feature support does not establish relative quality.
Which is available from more inference providers, Llama 3.1 8B or Jev?+
Llama 3.1 8B has 1 sourced provider route; Jev has 1, a tie.
Which offers better value, Llama 3.1 8B or Jev?+
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.