Qwen3 235B A22B Instruct 2507 vs Cydonia 24B V4.1
Cydonia 24B V4.1 costs 1.6x less per output token than Qwen3 235B A22B Instruct 2507 ($0.50 vs $0.775 per million). Updated October 23, 2026; no benchmark has tested both models yet.
- Coding
- No data
- Overall
- No data
- Output $/M
- $0.775
- Coding
- No data
- Overall
- No data
- Output $/M
- $0.50
Qwen3 235B A22B Instruct 2507 or Cydonia 24B V4.1: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick Qwen3 235B A22B Instruct 2507 for a larger context window (262K against 131K tokens).
No benchmark has tested both yet. Qwen3 235B A22B Instruct 2507 has results on LiveCodeBench (Artificial Analysis run), SciCode and Terminal-Bench Hard (Artificial Analysis run); Cydonia 24B V4.1 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 235B A22B Instruct 2507 has a AA Intelligence Index result, 12.0.
Directional only
Web design
Design Arena: websites
No call
Only Qwen3 235B A22B Instruct 2507 has a Design Arena: websites result, 1058.
Directional only
Long documents
Listed context window
Qwen3 235B A22B Instruct 2507
Qwen3 235B A22B Instruct 2507 takes 262K tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3 235B A22B Instruct 2507
Qwen3 235B A22B Instruct 2507 $0.0005, Cydonia 24B V4.1 $0.0006 per turn.
List prices
At a glance
- Coding index
- Qwen3 235B A22B Instruct 2507 not benchmarked, Cydonia 24B V4.1 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 235B A22B Instruct 2507 $0.775 against Cydonia 24B V4.1 $0.50 per million output tokens, 1.6x apart.
- Context window
- Qwen3 235B A22B Instruct 2507 takes 262K tokens, Cydonia 24B V4.1 131K.
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 Index4 boards measured
| Benchmark | Qwen3 235B A22B Instruct 2507 | Cydonia 24B V4.1 | Leader |
|---|---|---|---|
| LiveCodeBench (Artificial Analysis run) | 78.8% | Not measured | Only Qwen3 235B A22B Instruct 2507 measured |
| SciCode | 41.4% | Not measured | Only Qwen3 235B A22B Instruct 2507 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 15.2% | Not measured | Only Qwen3 235B A22B Instruct 2507 measured |
| WeirdML | 38.7% | Not measured | Only Qwen3 235B A22B Instruct 2507 measured |
| Neither model has a result on the other 39 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | Qwen3 235B A22B Instruct 2507 | Cydonia 24B V4.1 | Leader |
|---|---|---|---|
| AA Intelligence Index | 12.0 | Not measured | Only Qwen3 235B A22B Instruct 2507 measured |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | Qwen3 235B A22B Instruct 2507 | Cydonia 24B V4.1 | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 53.2% | Not measured | Only Qwen3 235B A22B Instruct 2507 measured |
| Berkeley Function Calling Leaderboard (BFCL) | 52.1% | Not measured | Only Qwen3 235B A22B Instruct 2507 measured |
| Neither model has a result on the other 40 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index4 boards measured
| Benchmark | Qwen3 235B A22B Instruct 2507 | Cydonia 24B V4.1 | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 79.0% | Not measured | Only Qwen3 235B A22B Instruct 2507 measured |
| Humanity's Last Exam (Artificial Analysis run) | 15.9% | Not measured | Only Qwen3 235B A22B Instruct 2507 measured |
| ARC-AGI | 11.0% | Not measured | Only Qwen3 235B A22B Instruct 2507 measured |
| ARC-AGI-2 | 1.3% | Not measured | Only Qwen3 235B A22B Instruct 2507 measured |
| Neither model has a result on the other 19 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | Qwen3 235B A22B Instruct 2507 | Cydonia 24B V4.1 | Leader |
|---|---|---|---|
| AA Math Index | 91.0 | Not measured | Only Qwen3 235B A22B Instruct 2507 measured |
| AIME 2025 (Artificial Analysis run) | 91.0% | Not measured | Only Qwen3 235B A22B Instruct 2507 measured |
| Neither model has a result on the other 12 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | Qwen3 235B A22B Instruct 2507 | Cydonia 24B V4.1 | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 84.3% | Not measured | Only Qwen3 235B A22B Instruct 2507 measured |
| Neither model has a result on the other 19 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index6 boards measured
| Benchmark | Qwen3 235B A22B Instruct 2507 | Cydonia 24B V4.1 | Leader |
|---|---|---|---|
| Design Arena: websites | 1058 | Not measured | Only Qwen3 235B A22B Instruct 2507 measured |
| Design Arena: UI components | 963 | Not measured | Only Qwen3 235B A22B Instruct 2507 measured |
| Design Arena: game dev | 951 | Not measured | Only Qwen3 235B A22B Instruct 2507 measured |
| Design Arena: data viz | 1070 | Not measured | Only Qwen3 235B A22B Instruct 2507 measured |
| Design Arena: 3D | 995 | Not measured | Only Qwen3 235B A22B Instruct 2507 measured |
| Design Arena: all code categories | 1038 | Not measured | Only Qwen3 235B A22B Instruct 2507 measured |
| Neither model has a result on the other 3 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index4 boards measured
| Benchmark | Qwen3 235B A22B Instruct 2507 | Cydonia 24B V4.1 | Leader |
|---|---|---|---|
| IFBench | 51.2% | Not measured | Only Qwen3 235B A22B Instruct 2507 measured |
| Long context reasoning (AA-LCR) | 72.0% | Not measured | Only Qwen3 235B A22B Instruct 2507 measured |
| DTBench | 78.4% | Not measured | Only Qwen3 235B A22B Instruct 2507 measured |
| LMCA | 23.5% | Not measured | Only Qwen3 235B A22B Instruct 2507 measured |
| Neither model has a result on the other 44 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 235B A22B Instruct 2507 costs less
Repository review
50K input + 3K output tokens
Qwen3 235B A22B Instruct 2507 costs less
Agent loop
200K input + 10K output tokens
Qwen3 235B A22B Instruct 2507 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 235B A22B Instruct 2507 | TheDrummer Cydonia 24B V4.1 |
|---|---|---|
| Maker | Qwen | TheDrummer |
| Weights | Open weights | Open weights |
| Released | July 21, 2025 | September 27, 2025 |
| Context window | 262K | 131K |
| Input $/Mtok | $0.145 | $0.30 |
| Output $/Mtok | $0.775 | $0.50 |
| 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 235B A22B Instruct 2507 better than Cydonia 24B V4.1 for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Qwen3 235B A22B Instruct 2507 for a larger context window (262K against 131K tokens).
Which is cheaper, Qwen3 235B A22B Instruct 2507 or Cydonia 24B V4.1?
Qwen3 235B A22B Instruct 2507 costs $0.145 per million input tokens and $0.775 per million output tokens; Cydonia 24B V4.1 costs $0.30 and $0.50. Cost per solved task is known for neither of them.
Which has the bigger context window, Qwen3 235B A22B Instruct 2507 or Cydonia 24B V4.1?
Qwen3 235B A22B Instruct 2507, with 262K tokens against 131K.
Has any benchmark tested both Qwen3 235B A22B Instruct 2507 and Cydonia 24B V4.1?
No benchmark has tested both yet. Qwen3 235B A22B Instruct 2507 has results on LiveCodeBench (Artificial Analysis run), SciCode and Terminal-Bench Hard (Artificial Analysis run); Cydonia 24B V4.1 has none yet. Compare them on price and context instead. Data as of October 23, 2026.