o4 Mini High vs Qwen3 14B
Updated October 21, 2026. 0 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
- Coding
- No data
- Overall
- No data
- Output $/M
- $4.40
- Coding
- No data
- Overall
- 6.7
- Output $/M
- $0.23
Decision reading
Neither model has a coding score yet, so the two cannot be ranked on coding.
o4 Mini High has the edge where the context window is larger, 200K against 41K tokens.
- Shared results
- 0
- o4 Mini High only
- 2
- Qwen3 14B only
- 18
- Like-for-like categories
- 0 of 9
Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.
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
o4 Mini High
o4 Mini High takes 200K tokens against 41K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3 14B
o4 Mini High $0.0033, Qwen3 14B $0.0002 per turn.
List prices
At a glance
- Coding index
- o4 Mini High not benchmarked, Qwen3 14B not benchmarked.
- Widest category
- No category has a result for both models.
- Widest single gap
- No benchmark has a result for both models.
- Output price
- o4 Mini High $4.40 against Qwen3 14B $0.23 per million output tokens, 19.1x apart.
- Context window
- o4 Mini High takes 200K tokens, Qwen3 14B 41K.
Benchmark by benchmark
First the score: boards each model leads, per category, where both have a result. Then every board, a category to a fold; bars are each result on a 0 to 100 scale, so compare the two in a row, not one row with another.
CodingDirectional onlySix of these boards feed the Vibe Coding Index4 boards, none shared
| Benchmark | o4 Mini High | Qwen3 14B | Leader |
|---|---|---|---|
| LiveCodeBench (Artificial Analysis run) | Not measured | 52.3% | Only Qwen3 14B measured |
| SciCode | Not measured | 30.7% | Only Qwen3 14B measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 5.3% | Only Qwen3 14B measured |
| ALE-Bench | 826.2 | Not measured | Only o4 Mini High measured |
| Neither model has a result on the other 34 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | o4 Mini High | Qwen3 14B | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 6.7 | Only Qwen3 14B measured |
| Epoch Capabilities Index | Not measured | 138.2 | Only Qwen3 14B measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | o4 Mini High | Qwen3 14B | Leader |
|---|---|---|---|
| τ²-Bench Telecom | Not measured | 34.5% | Only Qwen3 14B measured |
| GDPval | 25.3% | Not measured | Only o4 Mini High measured |
| Neither model has a result on the other 37 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
| Benchmark | o4 Mini High | Qwen3 14B | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | Not measured | 60.4% | Only Qwen3 14B measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 4.5% | Only Qwen3 14B measured |
| GPQA Diamond (Epoch AI run) | Not measured | 63.8% | 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, none shared
| Benchmark | o4 Mini High | Qwen3 14B | Leader |
|---|---|---|---|
| AA Math Index | Not measured | 58.0 | Only Qwen3 14B measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 58.0% | Only Qwen3 14B measured |
| OTIS Mock AIME | Not measured | 66.4% | 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, none shared
| Benchmark | o4 Mini High | Qwen3 14B | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | Not measured | 77.4% | Only Qwen3 14B measured |
| Neither model has a result on the other 16 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | o4 Mini High | Qwen3 14B | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | o4 Mini High | Qwen3 14B | Leader |
|---|---|---|---|
| IFBench | Not measured | 40.5% | Only Qwen3 14B measured |
| Long context reasoning (AA-LCR) | Not measured | 0.0% | Only Qwen3 14B measured |
| DTBench | Not measured | 64.0% | Only Qwen3 14B measured |
| LMCA | Not measured | 18.2% | Only Qwen3 14B measured |
| Fiction.LiveBench | Not measured | 62.5% | Only Qwen3 14B measured |
| Neither model has a result on the other 41 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | o4 Mini High | Qwen3 14B | Leader |
|---|---|---|---|
| Neither model has a result on 5 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
Qwen3 14B costs less
Repository review
50K input + 3K output tokens
Qwen3 14B costs less
Agent loop
200K input + 10K output tokens
Qwen3 14B 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 | OpenAI o4 Mini High | Qwen3 14B |
|---|---|---|
| Maker | OpenAI | Qwen |
| Weights | No public weights listed | Open weights |
| Released | April 16, 2025 | April 28, 2025 |
| Context window | 200K | 41K |
| Input $/Mtok | $1.10 | $0.11 |
| Output $/Mtok | $4.40 | $0.23 |
| 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 o4 Mini High better than Qwen3 14B for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. o4 Mini High has the edge where the context window is larger, 200K against 41K tokens.
Which is cheaper, o4 Mini High or Qwen3 14B?
o4 Mini High costs $1.10 per million input tokens and $4.40 per million output tokens; Qwen3 14B costs $0.11 and $0.23. Cost per solved task is known for neither of them.
Which has the bigger context window, o4 Mini High or Qwen3 14B?
o4 Mini High, with 200K tokens against 41K.
How many benchmarks is the o4 Mini High vs Qwen3 14B comparison based on?
0 benchmarks have a result for both models as of October 21, 2026. o4 Mini High's coding index rests on 0 independent benchmarks and Qwen3 14B's on 0. Where only one model has a result, the page says so instead of naming a winner.