Qwen3 14B vs Hy-MT2-1.8B
Hy-MT2-1.8B costs 1.3x less per output token than Qwen3 14B ($0.177 vs $0.23 per million). Updated October 22, 2026; no benchmark has tested both models yet.
- Coding
- No data
- Overall
- No data
- Output $/M
- $0.23
- Coding
- No data
- Overall
- No data
- Output $/M
- $0.177
Qwen3 14B or Hy-MT2-1.8B: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick Qwen3 14B for a larger context window (41K against 8K tokens).
No benchmark has tested both yet. Qwen3 14B has results on LiveCodeBench (Artificial Analysis run), SciCode and Terminal-Bench Hard (Artificial Analysis run); Hy-MT2-1.8B has none yet. Compare them on price and context instead.
Which one for your work
Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.
Coding
Vibe Coding Index, six public coding benchmarks
No call
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
No call
Only Qwen3 14B has a AA Intelligence Index result, 6.7.
Directional only
Long documents
Listed context window
Qwen3 14B
Qwen3 14B takes 41K tokens against 8K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Hy-MT2-1.8B
Qwen3 14B $0.0002, Hy-MT2-1.8B $0.0001 per turn.
List prices
At a glance
- Coding index
- Qwen3 14B not benchmarked, Hy-MT2-1.8B not benchmarked.
- Widest category
- None yet: no category has been tested on both models.
- Widest single gap
- None yet: no benchmark has tested both models.
- Output price
- Qwen3 14B $0.23 against Hy-MT2-1.8B $0.177 per million output tokens, 1.3x apart.
- Context window
- Qwen3 14B takes 41K tokens, Hy-MT2-1.8B 8K.
Benchmark by benchmark
Every board either model has a result on, a category to a fold; bars are each result on a 0 to 100 scale. Only one model is measured on each, so no row names a leader.
CodingDirectional onlySix of these boards feed the Vibe Coding Index3 boards measured
| Benchmark | Qwen3 14B | Hy-MT2-1.8B | Leader |
|---|---|---|---|
| LiveCodeBench (Artificial Analysis run) | 52.3% | Not measured | Only Qwen3 14B measured |
| SciCode | 30.7% | Not measured | Only Qwen3 14B measured |
| Terminal-Bench Hard (Artificial Analysis run) | 5.3% | Not measured | Only Qwen3 14B measured |
| Neither model has a result on the other 36 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | Qwen3 14B | Hy-MT2-1.8B | Leader |
|---|---|---|---|
| AA Intelligence Index | 6.7 | Not measured | Only Qwen3 14B measured |
| Epoch Capabilities Index | 138.2 | Not measured | Only Qwen3 14B measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | Qwen3 14B | Hy-MT2-1.8B | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 34.5% | Not measured | Only Qwen3 14B measured |
| Berkeley Function Calling Leaderboard (BFCL) | 41.0% | Not measured | Only Qwen3 14B measured |
| Neither model has a result on the other 39 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index4 boards measured
| Benchmark | Qwen3 14B | Hy-MT2-1.8B | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 60.4% | Not measured | Only Qwen3 14B measured |
| Humanity's Last Exam (Artificial Analysis run) | 4.5% | Not measured | Only Qwen3 14B measured |
| GPQA Diamond (Epoch AI run) | 63.8% | Not measured | Only Qwen3 14B measured |
| Kagi LLM Benchmark | 49.1% | Not measured | Only Qwen3 14B measured |
| Neither model has a result on the other 18 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index3 boards measured
| Benchmark | Qwen3 14B | Hy-MT2-1.8B | Leader |
|---|---|---|---|
| AA Math Index | 58.0 | Not measured | Only Qwen3 14B measured |
| AIME 2025 (Artificial Analysis run) | 58.0% | Not measured | Only Qwen3 14B measured |
| OTIS Mock AIME | 66.4% | Not measured | Only Qwen3 14B measured |
| Neither model has a result on the other 10 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | Qwen3 14B | Hy-MT2-1.8B | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 77.4% | Not measured | Only Qwen3 14B measured |
| Neither model has a result on the other 17 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index5 boards measured
| Benchmark | Qwen3 14B | Hy-MT2-1.8B | Leader |
|---|---|---|---|
| IFBench | 40.5% | Not measured | Only Qwen3 14B measured |
| Long context reasoning (AA-LCR) | 0.0% | Not measured | Only Qwen3 14B measured |
| DTBench | 64.0% | Not measured | Only Qwen3 14B measured |
| LMCA | 18.2% | Not measured | Only Qwen3 14B measured |
| Fiction.LiveBench | 62.5% | Not measured | Only Qwen3 14B measured |
| Neither model has a result on the other 41 boards here | |||
What each workload costs
Three fixed token mixes at each model's listed price, so per-token rates become a bill.
Chat turn
1K input + 500 output tokens
Hy-MT2-1.8B costs less
Repository review
50K input + 3K output tokens
Hy-MT2-1.8B costs less
Agent loop
200K input + 10K output tokens
Hy-MT2-1.8B costs less
Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.
Specification differences
The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.
| Specification | Qwen3 14B | Tencent Hy-MT2-1.8B |
|---|---|---|
| Maker | Qwen | Tencent |
| Weights | Open weights | Open weights |
| Released | April 28, 2025 | August 20, 2026 |
| Context window | 41K | 8K |
| Input $/Mtok | $0.11 | $0.044 |
| Output $/Mtok | $0.23 | $0.177 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Qwen3 14B better than Hy-MT2-1.8B for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Qwen3 14B for a larger context window (41K against 8K tokens).
Which is cheaper, Qwen3 14B or Hy-MT2-1.8B?
Qwen3 14B costs $0.11 per million input tokens and $0.23 per million output tokens; Hy-MT2-1.8B costs $0.044 and $0.177. Cost per solved task is known for neither of them.
Which has the bigger context window, Qwen3 14B or Hy-MT2-1.8B?
Qwen3 14B, with 41K tokens against 8K.
Has any benchmark tested both Qwen3 14B and Hy-MT2-1.8B?
No benchmark has tested both yet. Qwen3 14B has results on LiveCodeBench (Artificial Analysis run), SciCode and Terminal-Bench Hard (Artificial Analysis run); Hy-MT2-1.8B has none yet. Compare them on price and context instead. Data as of October 22, 2026.