Model comparison
Claude Opus 4.8 vs Qwen3 14B
Claude Opus 4.8 is the stronger model overall, scoring 60.7 to 35.5 on the Noometry Index. Qwen3 14B costs 16× less per token, which makes it the better buy when Claude Opus 4.8's lead doesn't matter for your workload.
Last verified . 9 shared benchmarks.
Alibaba (Qwen)Summary
- They share 9 benchmarks with published results for both. Claude Opus 4.8 scores higher in 6 categories and Qwen3 14B in 0 categories; 6 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where Claude Opus 4.8 leads 64.7 to 18.5.
- The biggest single-benchmark swing is Kagi LLM Benchmark: 88.8% for Claude Opus 4.8 and 49.1% for Qwen3 14B.
- Qwen3 14B is cheaper at $0.35 / $1.40 per million input/output tokens, against $5 / $25 for Claude Opus 4.8.
- Claude Opus 4.8 accepts more context: 1M tokens versus 131K.
- Qwen3 14B has downloadable open weights; the other is API-only.
Side by side
| Claude Opus 4.8 | Qwen3 14B | |
|---|---|---|
| Provider | Anthropic | Alibaba (Qwen) |
| Noometry Index | 60.7 | 35.5 |
| Released | 2026-05-28 | 2025-04 |
| Weights | Proprietary | Open |
| Context window | 1M | 131K |
| Max output | 128K | 8K |
| Input $ / M tokens | $5 | $0.35 |
| Output $ / M tokens | $25 | $1.40 |
| Results tracked | 65 | 12 |
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Category by category
Coding Claude Opus 4.8 leads
Claude Opus 4.8: 59.9 (#12), Qwen3 14B: 37.3 (#195)
| Benchmark | Claude Opus 4.8 | Qwen3 14B |
|---|---|---|
| SciCode | 53.5% | 31.6% |
| DeepSWE | 59% | — |
| FrontierCode | 46.5% | — |
| LMArena WebDev | 1556 | — |
| GSO | 47.1% | — |
| WeirdML | 82.9% | — |
| LMArena Coding | 1490 | — |
| ALE-Bench | 1,564 | — |
Agentic & Tool Use Claude Opus 4.8 leads
Claude Opus 4.8: 47.6 (#11), Qwen3 14B: 29.6 (#83)
| Benchmark | Claude Opus 4.8 | Qwen3 14B |
|---|---|---|
| APEX-Agents | 48.9% | — |
| Berkeley Function Calling Leaderboard | — | 41% |
| OSWorld 2.0 | 20.6% | — |
| Remote Labor Index | 8.3% | — |
| τ²-bench Banking | 39.7% | — |
| DeepResearch Bench | 50.2% | — |
| PostTrainBench | 33.8% | — |
| GBAEval | 70.9% | — |
| GDP.pdf | 24% | — |
| LMArena Search | 1204 | — |
| Vending-Bench 2 | 5,787 | — |
Reasoning Claude Opus 4.8 leads
Claude Opus 4.8: 64.7 (#16), Qwen3 14B: 18.5 (#280)
| Benchmark | Claude Opus 4.8 | Qwen3 14B |
|---|---|---|
| Kagi LLM Benchmark | 88.8% | 49.1% |
| CritPt | 20.9% | 0% |
| Chess Puzzles | 34% | 4% |
| DTBench | 94.9% | 64% |
| LMCA | 57.5% | 18.2% |
| Epoch Capabilities Index | 158.21 | 138.23 |
| ARC-AGI-2 | 72.1% | — |
| SimpleBench | 64.8% | — |
| NYT Connections (extended) | 91.1% | — |
| ARC-AGI-1 | 92.5% | — |
| EnigmaEval | 23.5% | — |
| EBR-Bench | 28.6% | — |
| LMArena Hard Prompts | 1482 | — |
| Mystery Game Puzzles | 36% | — |
| Surface Evolver Bench | 87.5% | — |
| Bench to the Future 3 | 0.14 | — |
| ForecastBench | 59.9 | — |
Math Claude Opus 4.8 leads
Claude Opus 4.8: 78.4 (#13), Qwen3 14B: 38.6 (#133)
| Benchmark | Claude Opus 4.8 | Qwen3 14B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 98.3% | 66.4% |
| FrontierMath (Tiers 1-3) | 80% | — |
| FrontierMath Tier 4 | 56.1% | — |
| MathArena Final-Answer Competitions | 91.8% | — |
| ProofBench | 69% | — |
| LMArena Math | 1487 | — |
| FrontierMath (Feb 2025 set) | 47.2% | — |
| FrontierMath Tier 4 (v1) | 31.3% | — |
Knowledge Claude Opus 4.8 leads
Claude Opus 4.8: 61.3 (#29), Qwen3 14B: 39.3 (#134)
| Benchmark | Claude Opus 4.8 | Qwen3 14B |
|---|---|---|
| GPQA Diamond | 91% | 63.8% |
| SimpleQA Verified | 53% | — |
| Vectara Hallucination Rate | — | 5.4% |
| LMArena Expert | 1502 | — |
Multimodal Not comparable
Claude Opus 4.8: 42.9 (#26), Qwen3 14B: —
| Benchmark | Claude Opus 4.8 | Qwen3 14B |
|---|---|---|
| LMArena Vision | 1294 | — |
| Blueprint-Bench 2 | 14.5% | — |
| Furniture Assembly | 42.5% | — |
| LMArena Document | 1475 | — |
Multilingual Not comparable
Claude Opus 4.8: 55.2 (#33), Qwen3 14B: —
| Benchmark | Claude Opus 4.8 | Qwen3 14B |
|---|---|---|
| LMArena Non-English | 1450 | — |
| LMArena Chinese | 1507 | — |
| LMArena French | 1481 | — |
| LMArena German | 1472 | — |
| LMArena Japanese | 1440 | — |
| LMArena Korean | 1432 | — |
| LMArena Russian | 1474 | — |
| LMArena Spanish | 1466 | — |
Instruction Following Not comparable
Claude Opus 4.8: 77.4 (#24), Qwen3 14B: —
| Benchmark | Claude Opus 4.8 | Qwen3 14B |
|---|---|---|
| LMArena Instruction Following | 1476 | — |
Long Context Claude Opus 4.8 leads
Claude Opus 4.8: 45.4 (#35), Qwen3 14B: 38.1 (#204)
| Benchmark | Claude Opus 4.8 | Qwen3 14B |
|---|---|---|
| Fiction.LiveBench | — | 62.5% |
| LMArena Longer Query | 1483 | — |
Writing & Preference Not comparable
Claude Opus 4.8: 72.0 (#16), Qwen3 14B: —
| Benchmark | Claude Opus 4.8 | Qwen3 14B |
|---|---|---|
| LMArena Text | 1461 | — |
| LMArena Creative Writing | 1454 | — |
| EQ-Bench Creative Writing | 1840 | — |
| EQ-Bench 4 | 1281 | — |
| LMArena Multi-Turn | 1476 | — |
Frequently asked questions
Is Claude Opus 4.8 better than Qwen3 14B?
Claude Opus 4.8 is the stronger model overall, scoring 60.7 to 35.5 on the Noometry Index. Qwen3 14B costs 16× less per token, which makes it the better buy when Claude Opus 4.8's lead doesn't matter for your workload.
Which is cheaper, Claude Opus 4.8 or Qwen3 14B?
Qwen3 14B is cheaper. It lists at $0.35 per million input tokens and $1.40 per million output tokens; Claude Opus 4.8 lists at $5 and $25.
Is Claude Opus 4.8 or Qwen3 14B better for coding?
Claude Opus 4.8 scores higher on coding benchmarks: 59.9 versus 37.3 in the Noometry coding category.
Which has the bigger context window?
Claude Opus 4.8 does, with 1M tokens against 131K.
How many benchmarks do Claude Opus 4.8 and Qwen3 14B share?
9 benchmarks have published results for both models. Claude Opus 4.8 has 65 scored results on Noometry and Qwen3 14B has 12.