Qwen3 Coder Next vs Inkling
Inkling leads Qwen3 Coder Next on the Artificial Analysis Intelligence Index, 25.0 to 9.2, and Qwen3 Coder Next costs 5.1x less per output token ($0.80 vs $4.05 per million). Updated October 21, 2026; 8 benchmarks have a result for both.
Qwen3 Coder Next or Inkling: which to pick
Only Inkling has a coding score, 31.5 on the Vibe Coding Index; Qwen3 Coder Next has none, so the two cannot be ranked on coding.
Pick Qwen3 Coder Next for output tokens 5.1x cheaper ($0.80 vs $4.05 per million) and work measured by WeirdML, where it leads.
- Shared results
- 8
- Qwen3 Coder Next only
- 3
- Inkling only
- 94
- Like-for-like categories
- 5 of 9
Only benchmarks that tested both models count toward a pick; a result only one model has is shown but not compared.
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
Only Inkling has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
Inkling
Inkling leads, 25.0 against 9.2.
Same board, both measured
Agentic work
AA Agentic Index
Inkling
Inkling leads, 22.5 against 0.9.
Same board, both measured
Reasoning
GPQA Diamond
No call
Only Inkling has a GPQA Diamond result, 87.1%.
Directional only
Knowledge
MMLU Pro
No call
Only Inkling has a MMLU Pro result, 86.3%.
Directional only
Web design
Design Arena: websites
No call
Only Inkling has a Design Arena: websites result, 1224.
Directional only
General
Vals Index
No call
Only Inkling has a Vals Index result, 29.0%.
Directional only
Long documents
Listed context window
Inkling
Inkling takes 524K tokens against 262K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3 Coder Next
Qwen3 Coder Next $0.0005, Inkling $0.0030 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only Inkling has the number, $0.76.
Directional only
At a glance
- Coding index
- Qwen3 Coder Next not benchmarked, Inkling 31.5 from 6 benchmarks.
- Widest category
- General, 30.3 points apart on average over 1 shared benchmark.
- Widest single gap
- Long context reasoning (AA-LCR): Qwen3 Coder Next 47.0% against Inkling 77.3%, 30.3 points apart on a 0 to 100 scale.
- Output price
- Qwen3 Coder Next $0.80 against Inkling $4.05 per million output tokens, 5.1x apart.
- Context window
- Qwen3 Coder Next takes 262K tokens, Inkling 524K.
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.
| Category | Qwen3 Coder Next | Bars | Inkling | Ties |
|---|---|---|---|---|
| Coding | 1 | 2 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 0 | 2 | ||
| General | 0 | 1 | ||
| All shared boards | 1 | 7 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index12
| Benchmark | Qwen3 Coder Next | Inkling | Leader |
|---|---|---|---|
| LiveBench coding | Not measured | 64.0% | Only Inkling measured |
| LMArena WebDev | Not measured | 1414 | Only Inkling measured |
| AA Coding Index | 36.2 | 52.1 | Inkling |
| LiveCodeBench (Vals AI) | Not measured | 85.5% | Only Inkling measured |
| SWE-bench Verified (Vals AI) | Not measured | 77.6% | Only Inkling measured |
| Terminal-Bench 4 (Vals AI) | Not measured | 0.5% | Only Inkling measured |
| Vibe Code Bench (Vals AI) | Not measured | 19.2% | Only Inkling measured |
| Code migration (Vals AI) | Not measured | 11.8% | Only Inkling measured |
| ProgramBench (Vals AI) | Not measured | 0.0% | Only Inkling measured |
| IOI (Vals AI) | Not measured | 14.9% | Only Inkling measured |
| Terminal-Bench 2.1 (Vals AI) | Not measured | 47.6% | Only Inkling measured |
| Vibe Code Bench 1-100 (Vals AI) | Not measured | 7.3% | Only Inkling measured |
| SkillsBench (Vals AI) | Not measured | 26.1% | Only Inkling measured |
| CyberBench (Vals AI) | Not measured | 66.8% | Only Inkling measured |
| SciCode | 36.2% | 47.0% | Inkling |
| Terminal-Bench Hard (Artificial Analysis run) | 18.2% | Not measured | Only Qwen3 Coder Next measured |
| FrontierCode | Not measured | 14.0% | Only Inkling measured |
| FrontierSWE | Not measured | 4.1% | Only Inkling measured |
| WeirdML | 34.4% | 32.3% | Qwen3 Coder Next |
| ALE-Bench | Not measured | 946.0 | Only Inkling measured |
| LMArena Text: coding | Not measured | 1466 | Only Inkling measured |
| LMArena Text: software and IT | Not measured | 1465 | Only Inkling measured |
| LMArena WebDev: React | Not measured | 1404 | Only Inkling measured |
| LMArena WebDev: HTML | Not measured | 1399 | Only Inkling measured |
| Neither model has a result on the other 15 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Qwen3 Coder Next | Inkling | Leader |
|---|---|---|---|
| AA Intelligence Index | 9.2 | 25.0 | Inkling |
| Epoch Capabilities Index | Not measured | 148.4 | Only Inkling measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Qwen3 Coder Next | Inkling | Leader |
|---|---|---|---|
| AA Agentic Index | 0.9 | 22.5 | Inkling |
| Design Arena: agents building web apps | Not measured | 1196 | Only Inkling measured |
| LiveBench research | Not measured | 59.9% | Only Inkling measured |
| LiveBench browser | Not measured | 16.0% | Only Inkling measured |
| LiveBench long agentic | Not measured | 14.1% | Only Inkling measured |
| SREBench (Vals AI) | Not measured | 0.0% | Only Inkling measured |
| Finance Agent (Vals AI) | Not measured | 46.6% | Only Inkling measured |
| Tax Agent Bench (Vals AI) | Not measured | 15.3% | Only Inkling measured |
| Legal Research Bench (Vals AI) | Not measured | 28.4% | Only Inkling measured |
| Harvey's Legal Agent Benchmark (Vals AI) | Not measured | 3.8% | Only Inkling measured |
| Excel Modeling Benchmark (Vals AI) | Not measured | 38.4% | Only Inkling measured |
| τ²-Bench Telecom | 79.5% | Not measured | Only Qwen3 Coder Next measured |
| APEX-Agents | Not measured | 33.8% | Only Inkling measured |
| Neither model has a result on the other 28 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Qwen3 Coder Next | Inkling | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 87.1% | Only Inkling measured |
| LiveBench reasoning | Not measured | 78.3% | Only Inkling measured |
| MMMU Pro (Vals AI) | Not measured | 78.5% | Only Inkling measured |
| GPQA Diamond (Artificial Analysis run) | 73.7% | 87.2% | Inkling |
| Humanity's Last Exam (Artificial Analysis run) | 10.1% | 31.9% | Inkling |
| GPQA Diamond (Epoch AI run) | Not measured | 88.3% | Only Inkling measured |
| ARC-AGI | Not measured | 79.5% | Only Inkling measured |
| ARC-AGI-2 | Not measured | 36.5% | Only Inkling measured |
| SimpleBench | Not measured | 50.0% | Only Inkling measured |
| Chess puzzles | Not measured | 21.0% | Only Inkling measured |
| LMArena Text: hard prompts | Not measured | 1451 | Only Inkling measured |
| LMArena Text: expert | Not measured | 1466 | Only Inkling measured |
| LMArena Text: hard prompts in English | Not measured | 1448 | Only Inkling measured |
| Neither model has a result on the other 9 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | Qwen3 Coder Next | Inkling | Leader |
|---|---|---|---|
| OTIS Mock AIME | Not measured | 88.9% | Only Inkling measured |
| FrontierMath, tiers 1 to 3 | Not measured | 33.3% | Only Inkling measured |
| FrontierMath, tier 4 | Not measured | 4.9% | Only Inkling measured |
| ProofBench | Not measured | 0.0% | Only Inkling measured |
| LMArena Text: math | Not measured | 1479 | Only Inkling measured |
| LMArena Text: mathematical work | Not measured | 1460 | Only Inkling measured |
| Neither model has a result on the other 7 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index11 boards, none shared
| Benchmark | Qwen3 Coder Next | Inkling | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 86.3% | Only Inkling measured |
| LegalBench (Vals AI) | Not measured | 83.0% | Only Inkling measured |
| CorpFin (Vals AI) | Not measured | 68.6% | Only Inkling measured |
| TaxEval (Vals AI) | Not measured | 75.3% | Only Inkling measured |
| MortgageTax (Vals AI) | Not measured | 64.0% | Only Inkling measured |
| MedCode (Vals AI) | Not measured | 41.2% | Only Inkling measured |
| SimpleQA Verified | Not measured | 40.3% | Only Inkling measured |
| LMArena Text: science | Not measured | 1455 | Only Inkling measured |
| LMArena Text: medicine and healthcare | Not measured | 1447 | Only Inkling measured |
| LMArena Text: legal and government | Not measured | 1448 | Only Inkling measured |
| LMArena Text: business and finance | Not measured | 1438 | Only Inkling measured |
| Neither model has a result on the other 7 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index9 boards, none shared
| Benchmark | Qwen3 Coder Next | Inkling | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 1224 | Only Inkling measured |
| Design Arena: UI components | Not measured | 1201 | Only Inkling measured |
| Design Arena: game dev | Not measured | 1166 | Only Inkling measured |
| Design Arena: data viz | Not measured | 1159 | Only Inkling measured |
| Design Arena: 3D | Not measured | 1161 | Only Inkling measured |
| Design Arena: all code categories | Not measured | 1206 | Only Inkling measured |
| Design Arena: SVG | Not measured | 1110 | Only Inkling measured |
| Design Arena: ASCII art | Not measured | 1089 | Only Inkling measured |
| LiveBench design | Not measured | 58.3% | Only Inkling measured |
GeneralLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Qwen3 Coder Next | Inkling | Leader |
|---|---|---|---|
| Vals Index | Not measured | 29.0% | Only Inkling measured |
| LiveBench data analysis | Not measured | 72.8% | Only Inkling measured |
| Public Benefits Bench (Vals AI) | Not measured | 58.5% | Only Inkling measured |
| SAGE (Vals AI) | Not measured | 36.5% | Only Inkling measured |
| MedScribe (Vals AI) | Not measured | 85.4% | Only Inkling measured |
| Vals Multimodal Index | Not measured | 49.4% | Only Inkling measured |
| IFBench | 35.2% | Not measured | Only Qwen3 Coder Next measured |
| Long context reasoning (AA-LCR) | 47.0% | 77.3% | Inkling |
| DTBench | Not measured | 87.5% | Only Inkling measured |
| LMCA | Not measured | 37.6% | Only Inkling measured |
| LMArena Text | Not measured | 1442 | Only Inkling measured |
| LMArena Text: instruction following | Not measured | 1425 | Only Inkling measured |
| LMArena Text: creative writing | Not measured | 1390 | Only Inkling measured |
| LMArena Text: English | Not measured | 1442 | Only Inkling measured |
| LMArena Text: non-English | Not measured | 1434 | Only Inkling measured |
| LMArena Text: Chinese | Not measured | 1493 | Only Inkling measured |
| LMArena Text: German | Not measured | 1445 | Only Inkling measured |
| LMArena Text: Japanese | Not measured | 1429 | Only Inkling measured |
| LMArena Text: Korean | Not measured | 1403 | Only Inkling measured |
| LMArena Text: French | Not measured | 1455 | Only Inkling measured |
| LMArena Text: Spanish | Not measured | 1449 | Only Inkling measured |
| LMArena Text: Russian | Not measured | 1430 | Only Inkling measured |
| LMArena Text: Polish | Not measured | 1450 | Only Inkling measured |
| LMArena Text: multi-turn | Not measured | 1437 | Only Inkling measured |
| LMArena Text: longer queries | Not measured | 1435 | Only Inkling measured |
| LMArena Text: writing and language | Not measured | 1404 | Only Inkling measured |
| LMArena Text: entertainment and media | Not measured | 1394 | Only Inkling measured |
| Neither model has a result on the other 19 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Qwen3 Coder Next | Inkling | 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 Coder Next costs less
Repository review
50K input + 3K output tokens
Qwen3 Coder Next costs less
Agent loop
200K input + 10K output tokens
Qwen3 Coder Next 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 Coder Next | Thinking Machines Inkling |
|---|---|---|
| Maker | Qwen | Thinking Machines |
| Weights | Open weights | Open weights |
| Released | February 4, 2026 | July 17, 2026 |
| Context window | 262K | 524K |
| Input $/Mtok | $0.12 | $0.975 |
| Output $/Mtok | $0.80 | $4.05 |
| Coding index | Not benchmarked | 31.5 |
| Independent measurements behind it | 0 | 6 |
| Cost per solved task | Not computable | $0.76 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Qwen3 Coder Next better than Inkling for coding?
Only Inkling has a coding score, 31.5 on the Vibe Coding Index; Qwen3 Coder Next has none, so the two cannot be ranked on coding. Pick Qwen3 Coder Next for output tokens 5.1x cheaper ($0.80 vs $4.05 per million) and work measured by WeirdML, where it leads.
Which is cheaper, Qwen3 Coder Next or Inkling?
Qwen3 Coder Next costs $0.12 per million input tokens and $0.80 per million output tokens; Inkling costs $0.975 and $4.05. Cost per solved task is known for Inkling only.
Which has the bigger context window, Qwen3 Coder Next or Inkling?
Inkling, with 524K tokens against 262K.
How many benchmarks is the Qwen3 Coder Next vs Inkling comparison based on?
8 benchmarks have a result for both models as of October 21, 2026. Qwen3 Coder Next's coding index rests on 0 independent benchmarks and Inkling's on 6. Where only one model has a result, the page says so instead of naming a winner.