Model comparison
GPT-5.2 vs Inkling
GPT-5.2 is the stronger model overall, scoring 54.1 to 44.1 on the Noometry Index. Inkling costs 1.9× less per token, which makes it the better buy when GPT-5.2's lead doesn't matter for your workload.
Last verified . 35 shared benchmarks.
Summary
- They share 35 benchmarks with published results for both. GPT-5.2 scores higher in 7 categories and Inkling in 2 categories; 6 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-5.2 leads 60.0 to 31.3.
- The biggest single-benchmark swing is WeirdML: 72.2% for GPT-5.2 and 32.3% for Inkling.
- Inkling is cheaper at $1.87 / $4.68 per million input/output tokens, against $1.75 / $14 for GPT-5.2.
- GPT-5.2 accepts more context: 400K tokens versus 66K.
- Inkling has downloadable open weights; the other is API-only.
Side by side
| GPT-5.2 | Inkling | |
|---|---|---|
| Provider | OpenAI | Thinking Machines Lab |
| Noometry Index | 54.1 | 44.1 |
| Released | 2025-12-11 | 2026-07-15 |
| Weights | Proprietary | Open |
| Context window | 400K | 66K |
| Max output | 128K | 66K |
| Input $ / M tokens | $1.75 | $1.87 |
| Output $ / M tokens | $14 | $4.68 |
| Results tracked | 67 | 41 |
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Category by category
Coding GPT-5.2 leads
GPT-5.2: 51.6 (#37), Inkling: 34.5 (#234)
| Benchmark | GPT-5.2 | Inkling |
|---|---|---|
| LMArena WebDev | 1416 | 1413 |
| WeirdML | 72.2% | 32.3% |
| LMArena Coding | 1447 | 1464 |
| ALE-Bench | 1,294 | 946 |
| SWE-bench Verified | 73.8% | — |
| FrontierCode | — | 14% |
| SWE-bench Verified (bash only) | 72.8% | — |
| SWE-bench Multilingual | 66.7% | — |
| FrontierSWE | — | 4.1% |
| SciCode | — | 47% |
| GSO | 27.4% | — |
| AlgoTune | 2.05 | — |
Agentic & Tool Use GPT-5.2 leads
GPT-5.2: 40.2 (#24), Inkling: 29.6 (#85)
| Benchmark | GPT-5.2 | Inkling |
|---|---|---|
| τ²-bench Banking | 32.2% | 25% |
| Terminal-Bench | 64.9% | — |
| APEX-Agents | — | 33.8% |
| Berkeley Function Calling Leaderboard | 55.9% | — |
| GDPval | 49.7% | — |
| Remote Labor Index | 2.5% | — |
| τ²-bench Airline | 83% | — |
| τ²-bench Retail | 81.6% | — |
| τ²-bench Telecom | 89.7% | — |
| DeepResearch Bench | 41.1% | — |
| LMArena Search | 1207 | — |
| METR Time Horizons | 75.3% | — |
| Vending-Bench 2 | 3,591 | — |
Reasoning GPT-5.2 leads
GPT-5.2: 50.2 (#35), Inkling: 40.4 (#56)
| Benchmark | GPT-5.2 | Inkling |
|---|---|---|
| ARC-AGI-2 | 52.9% | 36.5% |
| SimpleBench | 45.8% | 50% |
| ARC-AGI-1 | 86.2% | 79.5% |
| Chess Puzzles | 49% | 21% |
| LMArena Hard Prompts | 1445 | 1451 |
| DTBench | 90.9% | 87.5% |
| LMCA | 43.9% | 37.6% |
| Epoch Capabilities Index | 153.45 | 148.54 |
| Kagi LLM Benchmark | 73.3% | — |
| NYT Connections (extended) | 83.6% | — |
| CritPt | — | 5.4% |
| EnigmaEval | 10.4% | — |
| EBR-Bench | 23% | — |
| Mystery Game Puzzles | 23% | — |
| ForecastBench | 60.1 | — |
Math GPT-5.2 leads
GPT-5.2: 60.0 (#38), Inkling: 31.3 (#225)
| Benchmark | GPT-5.2 | Inkling |
|---|---|---|
| FrontierMath (Tiers 1-3) | 67.4% | 33.3% |
| FrontierMath Tier 4 | 31.7% | 4.9% |
| OTIS Mock AIME 2024-2025 | 96.1% | 88.9% |
| ProofBench | 15% | 0% |
| LMArena Math | 1440 | 1479 |
| MathArena Final-Answer Competitions | 72% | — |
| FrontierMath (Feb 2025 set) | 40.7% | — |
| FrontierMath Tier 4 (v1) | 18.8% | — |
Knowledge GPT-5.2 leads
GPT-5.2: 59.3 (#32), Inkling: 55.1 (#49)
| Benchmark | GPT-5.2 | Inkling |
|---|---|---|
| GPQA Diamond | 91.4% | 88.3% |
| SimpleQA Verified | 37.1% | 40.3% |
| LMArena Expert | 1445 | 1465 |
| Humanity's Last Exam | 27.8% | — |
| Vectara Hallucination Rate | 8.4% | — |
Multimodal Not comparable
GPT-5.2: 51.3 (#7), Inkling: —
| Benchmark | GPT-5.2 | Inkling |
|---|---|---|
| LMArena Vision | 1268 | — |
| VPCT | 84% | — |
| Furniture Assembly | 38.3% | — |
| LMArena Document | 1405 | — |
Multilingual Too close to call
GPT-5.2: 53.4 (#67), Inkling: 54.0 (#52)
| Benchmark | GPT-5.2 | Inkling |
|---|---|---|
| LMArena Non-English | 1425 | 1434 |
| LMArena Chinese | 1460 | 1490 |
| LMArena French | 1455 | 1458 |
| LMArena German | 1448 | 1446 |
| LMArena Japanese | 1420 | 1429 |
| LMArena Korean | 1392 | 1404 |
| LMArena Russian | 1440 | 1429 |
| LMArena Spanish | 1433 | 1448 |
Instruction Following Too close to call
GPT-5.2: 74.7 (#89), Inkling: 75.1 (#71)
| Benchmark | GPT-5.2 | Inkling |
|---|---|---|
| LMArena Instruction Following | 1417 | 1426 |
Long Context Too close to call
GPT-5.2: 44.0 (#78), Inkling: 43.8 (#86)
| Benchmark | GPT-5.2 | Inkling |
|---|---|---|
| LMArena Longer Query | 1428 | 1434 |
| CL-bench | 18.2% | — |
Writing & Preference GPT-5.2 leads
GPT-5.2: 66.8 (#32), Inkling: 65.2 (#51)
| Benchmark | GPT-5.2 | Inkling |
|---|---|---|
| LMArena Text | 1439 | 1441 |
| LMArena Creative Writing | 1401 | 1387 |
| EQ-Bench Creative Writing | 1703 | 1611 |
| LMArena Multi-Turn | 1458 | 1436 |
| EQ-Bench 4 | — | 1226 |
Frequently asked questions
Is GPT-5.2 better than Inkling?
GPT-5.2 is the stronger model overall, scoring 54.1 to 44.1 on the Noometry Index. Inkling costs 1.9× less per token, which makes it the better buy when GPT-5.2's lead doesn't matter for your workload.
Which is cheaper, GPT-5.2 or Inkling?
Inkling is cheaper. It lists at $1.87 per million input tokens and $4.68 per million output tokens; GPT-5.2 lists at $1.75 and $14.
Is GPT-5.2 or Inkling better for coding?
GPT-5.2 scores higher on coding benchmarks: 51.6 versus 34.5 in the Noometry coding category.
Which has the bigger context window?
GPT-5.2 does, with 400K tokens against 66K.
How many benchmarks do GPT-5.2 and Inkling share?
35 benchmarks have published results for both models. GPT-5.2 has 67 scored results on Noometry and Inkling has 41.