| Signal | Llama 3.1 8B Instruct | Delta | Kimi K2 0905 |
|---|---|---|---|
Capabilities | 50 | -- | |
Benchmarks | 51 | +1 | |
Pricing | 100 | +2 | |
Context window size | 67 | -14 | |
Recency | 19 | -74 | |
Output Capacity | 70 | +50 | |
| Overall Result | 3 wins | of 6 | 2 wins |
Score History
52.1
current score
Kimi K2 0905
right now
52.2
current score
Meta
Moonshot AI
Llama 3.1 8B Instruct saves you $135.50/month
That's $1626.00/year compared to Kimi K2 0905 at your current usage level of 100K calls/month.
| Metric | Llama 3.1 8B Instruct | Kimi K2 0905 | Winner |
|---|---|---|---|
| Overall Score | 52 | 52 | Kimi K2 0905 |
| Rank | #150 | #149 | Kimi K2 0905 |
| Quality Rank | #150 | #149 | Kimi K2 0905 |
| Adoption Rank | #150 | #149 | Kimi K2 0905 |
| Parameters | 8B | -- | -- |
| Context Window | 16K | 131K | Kimi K2 0905 |
| Pricing | $0.02/$0.05/M | $0.40/$2.00/M | -- |
| Signal Scores | |||
| Capabilities | 50 | 50 | Llama 3.1 8B Instruct |
| Benchmarks | 51 | 51 | Llama 3.1 8B Instruct |
| Pricing | 100 | 98 | Llama 3.1 8B Instruct |
| Context window size | 67 | 81 | Kimi K2 0905 |
| Recency | 19 | 94 | Kimi K2 0905 |
| Output Capacity | 70 | 20 | Llama 3.1 8B Instruct |
Our score (0-100) is driven by benchmark performance (90%) from Arena Elo ratings, MMLU, GPQA, HumanEval, SWE-bench, and 15+ standardized evaluations. Capabilities and context window serve as tiebreakers (10%). Learn more about our methodology.
Scores 52/100 (rank #150), placing it in the top 49% of all 290 models tracked.
Scores 52/100 (rank #149), placing it in the top 49% of all 290 models tracked.
With only a 0-point gap, these models are in the same performance tier. The practical difference in output quality is minimal - your choice should depend on pricing, latency requirements, and specific feature needs.
Llama 3.1 8B Instruct offers 97% better value per quality point. At 1M tokens/day, you'd spend $1.05/month with Llama 3.1 8B Instruct vs $36.00/month with Kimi K2 0905 - a $34.95 monthly difference.
Both models have comparable response speeds. For most applications, the latency difference is negligible.
When latency matters most: Interactive chatbots, IDE code completion, real-time translation, and user-facing applications where response time directly impacts experience. For batch processing, background summarization, or offline analysis, latency is less critical.
Code generation & review
Higher benchmark score (0/100) indicates stronger performance on coding tasks like generating functions, debugging, and refactoring
Customer support chatbot
Faster response time (speed score 0/100) is critical for user-facing chat. Llama 3.1 8B Instruct also offers lower per-token costs for high-volume support
Long document analysis
Larger context window (131K tokens) can process longer documents, contracts, and research papers in a single pass
Batch data extraction
Lower output pricing ($0.05/M) reduces costs when processing thousands of records daily
Creative writing & content
Higher overall composite score (52/100) correlates with better nuance, coherence, and style in long-form content
Llama 3.1 8B Instruct and Kimi K2 0905 are extremely close in overall performance (only 0.10000000000000142 points apart). Your best choice depends entirely on which specific strengths matter most for your use case.
Best for Quality
Llama 3.1 8B Instruct
Marginally better benchmark scores; both are excellent
Best for Cost
Llama 3.1 8B Instruct
97% lower pricing; better value at scale
Best for Reliability
Llama 3.1 8B Instruct
Higher uptime and faster response speeds
Best for Prototyping
Llama 3.1 8B Instruct
Stronger community support and better developer experience
Best for Production
Llama 3.1 8B Instruct
Wider enterprise adoption and proven at scale
by Meta
| Capability | Llama 3.1 8B Instruct | Kimi K2 0905 |
|---|---|---|
| Vision (Image Input) | ||
| Function Calling | ||
| Streaming | ||
| JSON Mode | ||
| Reasoning | ||
| Web Search | ||
| Image Output |
Meta
Moonshot AI
Llama 3.1 8B Instruct saves you $3.02/month
That's 97% cheaper than Kimi K2 0905 at 1,000 tokens/request and 100 requests/day.
Assumes 60% input / 40% output token ratio per request. Actual costs may vary based on your usage pattern.
| Parameter | Llama 3.1 8B Instruct | Kimi K2 0905 |
|---|---|---|
| Context Window | 16K | 131K |
| Max Output Tokens | 16,384 | -- |
| Open Source | Yes | Yes |
| Created | Jul 23, 2024 | Sep 4, 2025 |
Kimi K2 0905 scores 52/100 (rank #149) compared to Llama 3.1 8B Instruct's 52/100 (rank #150), giving it a 0-point advantage. Kimi K2 0905 is the stronger overall choice, though Llama 3.1 8B Instruct may excel in specific areas like cost efficiency.
Llama 3.1 8B Instruct is ranked #150 and Kimi K2 0905 is ranked #149 out of 290+ AI models. Rankings use a composite score combining benchmark performance (90%) from MMLU, GPQA, HumanEval, SWE-bench, and 15+ standardized evaluations, with capabilities and context window as tiebreakers (10%). Scores update hourly.
Llama 3.1 8B Instruct is cheaper at $0.05/M output tokens vs Kimi K2 0905's $2.00/M output tokens - 40.0x more expensive. Input token pricing: Llama 3.1 8B Instruct at $0.02/M vs Kimi K2 0905 at $0.40/M.
Kimi K2 0905 has a larger context window of 131,072 tokens compared to Llama 3.1 8B Instruct's 16,384 tokens. A larger context window means the model can process longer documents and conversations.