Model comparison
Mercury 2 vs Qwen3 235B-A22B
Qwen3 235B-A22B is the stronger model overall, scoring 43.5 to 39.1 on the Noometry Index. Mercury 2 costs 3.3× less per token, which makes it the better buy when Qwen3 235B-A22B's lead doesn't matter for your workload.
Last verified . 15 shared benchmarks.
Alibaba (Qwen)Summary
- They share 15 benchmarks with published results for both. Mercury 2 scores higher in 1 category and Qwen3 235B-A22B in 6 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where Qwen3 235B-A22B leads 49.6 to 36.2.
- Mercury 2 is cheaper at $0.25 / $0.75 per million input/output tokens, against $0.70 / $2.80 for Qwen3 235B-A22B.
- Qwen3 235B-A22B accepts more context: 131K tokens versus 128K.
- Qwen3 235B-A22B has downloadable open weights; the other is API-only.
Side by side
| Mercury 2 | Qwen3 235B-A22B | |
|---|---|---|
| Provider | Inception | Alibaba (Qwen) |
| Noometry Index | 39.1 | 43.5 |
| Released | 2026-02-20 | 2025-04 |
| Weights | Proprietary | Open |
| Context window | 128K | 131K |
| Max output | 50K | 16K |
| Input $ / M tokens | $0.25 | $0.70 |
| Output $ / M tokens | $0.75 | $2.80 |
| Results tracked | 17 | 49 |
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Category by category
Coding Qwen3 235B-A22B leads
Mercury 2: 33.5 (#255), Qwen3 235B-A22B: 44.3 (#75)
| Benchmark | Mercury 2 | Qwen3 235B-A22B |
|---|---|---|
| SciCode | 38.7% | 42.4% |
| WeirdML | 43.2% | 41% |
| LMArena Coding | 1391 | 1445 |
| Aider Polyglot | — | 59.6% |
| LMArena WebDev | 1171 | — |
| ALE-Bench | 785.58 | — |
Agentic & Tool Use Not comparable
Mercury 2: —, Qwen3 235B-A22B: 33.9 (#51)
| Benchmark | Mercury 2 | Qwen3 235B-A22B |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 52.1% |
| Vending-Bench 2 | — | -11.34 |
Reasoning Mercury 2 leads
Mercury 2: 23.8 (#170), Qwen3 235B-A22B: 15.7 (#311)
| Benchmark | Mercury 2 | Qwen3 235B-A22B |
|---|---|---|
| CritPt | 0.8% | 0% |
| LMArena Hard Prompts | 1362 | 1433 |
| ARC-AGI-2 | — | 1.3% |
| SimpleBench | — | 31% |
| Kagi LLM Benchmark | — | 69.4% |
| ARC-AGI-1 | — | 11% |
| Chess Puzzles | — | 12% |
| Mystery Game Puzzles | — | 9% |
| DTBench | — | 80.3% |
| LMCA | — | 29.3% |
| Epoch Capabilities Index | — | 143.85 |
| ForecastBench | — | 59.7 |
Math Not comparable
Mercury 2: —, Qwen3 235B-A22B: 50.4 (#57)
| Benchmark | Mercury 2 | Qwen3 235B-A22B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | — | 86.7% |
| Omni-MATH | — | 71.8% |
| LMArena Math | — | 1432 |
| MATH Level 5 | — | 68.9% |
| FrontierMath (Feb 2025 set) | — | 8.5% |
| FrontierMath Tier 4 (v1) | — | 0% |
Knowledge Qwen3 235B-A22B leads
Mercury 2: 36.2 (#172), Qwen3 235B-A22B: 49.6 (#73)
| Benchmark | Mercury 2 | Qwen3 235B-A22B |
|---|---|---|
| Vectara Hallucination Rate | 12.3% | 9.3% |
| LMArena Expert | 1358 | 1463 |
| GPQA Diamond | — | 80.1% |
| SimpleQA Verified | — | 40.4% |
| MMLU-Pro | — | 84.4% |
| Confabulations | — | 15.6% |
| GPQA (HELM) | — | 72.7% |
Multilingual Qwen3 235B-A22B leads
Mercury 2: 46.6 (#157), Qwen3 235B-A22B: 52.3 (#89)
| Benchmark | Mercury 2 | Qwen3 235B-A22B |
|---|---|---|
| LMArena Non-English | 1331 | 1409 |
| LMArena Chinese | 1417 | 1481 |
| LMArena Russian | 1304 | 1411 |
| LMArena French | — | 1445 |
| LMArena German | — | 1433 |
| LMArena Japanese | — | 1399 |
| LMArena Korean | — | 1391 |
| LMArena Spanish | — | 1430 |
Instruction Following Qwen3 235B-A22B leads
Mercury 2: 70.2 (#165), Qwen3 235B-A22B: 72.6 (#136)
| Benchmark | Mercury 2 | Qwen3 235B-A22B |
|---|---|---|
| LMArena Instruction Following | 1329 | 1408 |
| IFEval | — | 83.5% |
Long Context Qwen3 235B-A22B leads
Mercury 2: 40.5 (#154), Qwen3 235B-A22B: 46.1 (#26)
| Benchmark | Mercury 2 | Qwen3 235B-A22B |
|---|---|---|
| LMArena Longer Query | 1330 | 1426 |
| Fiction.LiveBench | — | 75% |
Writing & Preference Qwen3 235B-A22B leads
Mercury 2: 53.8 (#155), Qwen3 235B-A22B: 59.6 (#108)
| Benchmark | Mercury 2 | Qwen3 235B-A22B |
|---|---|---|
| LMArena Text | 1355 | 1419 |
| LMArena Creative Writing | 1289 | 1384 |
| LMArena Multi-Turn | 1358 | 1432 |
| Short-Story Creative Writing | — | 83% |
| EQ-Bench Creative Writing | — | 1366 |
| WildBench | — | 86.6% |
Frequently asked questions
Is Mercury 2 better than Qwen3 235B-A22B?
Qwen3 235B-A22B is the stronger model overall, scoring 43.5 to 39.1 on the Noometry Index. Mercury 2 costs 3.3× less per token, which makes it the better buy when Qwen3 235B-A22B's lead doesn't matter for your workload.
Which is cheaper, Mercury 2 or Qwen3 235B-A22B?
Mercury 2 is cheaper. It lists at $0.25 per million input tokens and $0.75 per million output tokens; Qwen3 235B-A22B lists at $0.70 and $2.80.
Is Mercury 2 or Qwen3 235B-A22B better for coding?
Qwen3 235B-A22B scores higher on coding benchmarks: 44.3 versus 33.5 in the Noometry coding category.
Which has the bigger context window?
Qwen3 235B-A22B does, with 131K tokens against 128K.
How many benchmarks do Mercury 2 and Qwen3 235B-A22B share?
15 benchmarks have published results for both models. Mercury 2 has 17 scored results on Noometry and Qwen3 235B-A22B has 49.