Model comparison
Llama2 70b Steerlm Chat vs Qwen3 14B
Qwen3 14B is the stronger model overall, scoring 35.5 to 31.8 on the Noometry Index.
Last verified . 0 shared benchmarks.
NVIDIA
Alibaba (Qwen)Summary
- The widest gap is in long context, where Qwen3 14B leads 38.1 to 30.4.
Side by side
| Llama2 70b Steerlm Chat | Qwen3 14B | |
|---|---|---|
| Provider | NVIDIA | Alibaba (Qwen) |
| Noometry Index | 31.8 | 35.5 |
| Released | — | 2025-04 |
| Weights | Open | Open |
| Context window | — | 131K |
| Max output | — | 8K |
| Input $ / M tokens | — | $0.35 |
| Output $ / M tokens | — | $1.40 |
| Results tracked | 9 | 12 |
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Category by category
Coding Qwen3 14B leads
Llama2 70b Steerlm Chat: 29.9 (#300), Qwen3 14B: 37.3 (#195)
| Benchmark | Llama2 70b Steerlm Chat | Qwen3 14B |
|---|---|---|
| SciCode | — | 31.6% |
| LMArena Coding | 1025 | — |
Agentic & Tool Use Not comparable
Llama2 70b Steerlm Chat: —, Qwen3 14B: 29.6 (#83)
| Benchmark | Llama2 70b Steerlm Chat | Qwen3 14B |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 41% |
Reasoning Llama2 70b Steerlm Chat leads
Llama2 70b Steerlm Chat: 20.0 (#246), Qwen3 14B: 18.5 (#280)
| Benchmark | Llama2 70b Steerlm Chat | Qwen3 14B |
|---|---|---|
| Kagi LLM Benchmark | — | 49.1% |
| CritPt | — | 0% |
| Chess Puzzles | — | 4% |
| LMArena Hard Prompts | 1047 | — |
| DTBench | — | 64% |
| LMCA | — | 18.2% |
| Epoch Capabilities Index | — | 138.23 |
Math Qwen3 14B leads
Llama2 70b Steerlm Chat: 31.3 (#226), Qwen3 14B: 38.6 (#133)
| Benchmark | Llama2 70b Steerlm Chat | Qwen3 14B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | — | 66.4% |
| LMArena Math | 1072 | — |
Knowledge Not comparable
Llama2 70b Steerlm Chat: —, Qwen3 14B: 39.3 (#134)
| Benchmark | Llama2 70b Steerlm Chat | Qwen3 14B |
|---|---|---|
| GPQA Diamond | — | 63.8% |
| Vectara Hallucination Rate | — | 5.4% |
Multilingual Not comparable
Llama2 70b Steerlm Chat: 28.8 (#270), Qwen3 14B: —
| Benchmark | Llama2 70b Steerlm Chat | Qwen3 14B |
|---|---|---|
| LMArena Non-English | 1063 | — |
Instruction Following Not comparable
Llama2 70b Steerlm Chat: 54.2 (#279), Qwen3 14B: —
| Benchmark | Llama2 70b Steerlm Chat | Qwen3 14B |
|---|---|---|
| LMArena Instruction Following | 1060 | — |
Long Context Qwen3 14B leads
Llama2 70b Steerlm Chat: 30.4 (#288), Qwen3 14B: 38.1 (#204)
| Benchmark | Llama2 70b Steerlm Chat | Qwen3 14B |
|---|---|---|
| Fiction.LiveBench | — | 62.5% |
| LMArena Longer Query | 998 | — |
Writing & Preference Not comparable
Llama2 70b Steerlm Chat: 31.6 (#283), Qwen3 14B: —
| Benchmark | Llama2 70b Steerlm Chat | Qwen3 14B |
|---|---|---|
| LMArena Text | 1098 | — |
| LMArena Creative Writing | 1091 | — |
| LMArena Multi-Turn | 1058 | — |
Frequently asked questions
Is Llama2 70b Steerlm Chat better than Qwen3 14B?
Qwen3 14B is the stronger model overall, scoring 35.5 to 31.8 on the Noometry Index.
Is Llama2 70b Steerlm Chat or Qwen3 14B better for coding?
Qwen3 14B scores higher on coding benchmarks: 37.3 versus 29.9 in the Noometry coding category.
How many benchmarks do Llama2 70b Steerlm Chat and Qwen3 14B share?
0 benchmarks have published results for both models. Llama2 70b Steerlm Chat has 9 scored results on Noometry and Qwen3 14B has 12.