text model comparison
GLM-5.3-Flash vs Inkling 2 Small
GLM-5.3-Flash and Inkling 2 Small are both current text models, from Z.ai and Thinking Machines Lab. GLM-5.3-Flash is 50% cheaper per 1M input tokens than Inkling 2 Small, $0.15 vs $0.30. In Thinking Machines Lab's Inkling 2 Small model card, GLM-5.3-Flash scores 63.4% on DeepSWE v1.1 and Inkling 2 Small 72.1%. GLM-5.3-Flash has the larger context window, 1,000,000 vs 262,144 tokens.
- Lower price per 1M input tokens
- GLM-5.3-Flash$0.15 against $0.30
- Longer context window
- GLM-5.3-Flash1M tokens against 262K tokens
- Newer release
- Inkling 2 SmallNov 2026 against Aug 2026
Data verified November 6, 2026 · Dataset 2026-11-06
Side by side
GLM-5.3-Flash and Inkling 2 Small, line by line
Prices in effect today, compared at the same tier wherever both list one. The better value in each row is highlighted.
| Fact | GLM-5.3-Flash | Inkling 2 Small |
|---|---|---|
| Price | ||
| Price per 1M input tokens | $0.15 (better) | $0.30 |
| Price per 1M output tokens | $0.50 (better) | $1 |
| Independent benchmarks | ||
| Arena Text | 1477 Elo (#20 of 52) | Not measured |
| Arena WebDev | 1607 Elo (#24 of 46) | Not measured |
| GPQA Diamond | 90.2% (#28 of 41) | Not measured |
| FrontierMath Tiers 1-3 (v2) | 55.8% (#33 of 41) | Not measured |
| Specs | ||
| Provider | Z.ai | Thinking Machines Lab |
| Release date | August 26, 2026 | November 5, 2026 |
| Access | API and open weights | API only |
| Context window | 1,000,000 tokens | 262,144 tokens |
| Max output | 128,000 tokens | Not reported |
| Input | text, image, video | text, image |
| Output | text | text |
No independent benchmark has scored both models yet. Each rank counts the current models scored on that benchmark.
Provider results
What the labs report
Results tables a lab published with one of its models, where it lists both GLM-5.3-Flash and Inkling 2 Small. These are the lab's own figures, not independent measurements, and each table is read on its own: labs run benchmarks with different harnesses and budgets.
Thinking Machines Lab, Inkling 2 Small model card, November 5, 2026Thinking Machines Lab · 2026-11-06
| Benchmark | GLM-5.3-Flash | Inkling 2 Small |
|---|---|---|
| DeepSWE v1.1 | 63.4% | 72.1% (better) |
| Vibe Code Bench v1.1 | 30.8% | 81.6% (better) |
| SWE-Marathon v1.1 | 38.8% | 38.8% |
| SWE-bench Multilingual | 88.9% (better) | 81.2% |
| SWE-bench Verified | 92.0% (better) | 82.2% |
| PostTrainBench v1.2 | 36.8% (better) | 31.9% |
| MCP Atlaspublic set | 82.0% | 85.0% (better) |
| EnterpriseBench-CoreCraft | 46.7% | 71.8% (better) |
| Finance Agent v2.1 | 60.6% | 67.5% (better) |
| Excel Modeling Benchmark v1.1 | 66.3% | 69.5% (better) |
| Tax Agent Bench | 62.0% | 65.8% (better) |
| APEX-Accounting v1.1pass@1 | 12.3% | 14.5% (better) |
| APEX-Accounting v1.1mean score | 53.7% | 62.0% (better) |
| Harvey LAB v1.1 | 10.4% | 21.7% (better) |
| Legal Research Bench | 45.2% | 46.6% (better) |
| BabyVision | 53.4% | 68.6% (better) |
| BabyVisionwith Python | 82.6% | 93.7% (better) |
| CharXiv (RQ) | 80.2% | 85.6% (better) |
| CharXiv (RQ)with Python | 89.4% (better) | 87.7% |
| Chartography | 16.3% | 36.7% (better) |
| Chartographywith Python | 78.0% | 80.4% (better) |
| MathVision | 82.9% | 92.8% (better) |
| SAGE v1.1 | 89.3% (better) | 88.6% |
| GPQA-Diamond | 91.2% | 93.0% (better) |
| LongBench v2 | 68.2% | 70.6% (better) |
| ComplexConstraints v1.0 | 27.6% | 44.5% (better) |
| CyberBench v1.1 | 67.9% | 72.7% (better) |
| PatchEval | 74.8% | 79.6% (better) |
Thinking Machines' table from the Inkling 2 Small model card, run at effort 0.99 (xhigh) with a 1M-token trajectory limit; where a benchmark's owner publishes scores (Datacurve, Scale AI, Surge, Mercor, Thoughtful, Vals), Thinking Machines uses them. DeepSeek-V4.1-Flash is the 1017 build. Left out here: the Artificial Analysis and Vals indexes, the rows Artificial Analysis ran (FrontierCode, Terminal-Bench, GDP.pdf, MMMU-Pro, HLE, CritPt, AA-LCR, AA-Omniscience, AutomationBench, EnterpriseOps-Gym, Tau 3 Banking), Arena and DesignArena ratings, PMPP-Hard (not a percentage), the safety rows, and JobBench, DAYJOB and Humanity's Sixth Sense, which we could not confirm are public.
Charts
Against every current text model
Where GLM-5.3-Flash and Inkling 2 Small land among the current models of their task, on every independent benchmark, on price and on context.
Where each stands
Only GLM-5.3-Flash has a current Arena Text score: #20 of 52. Each bar is the share of current models it beats.
Independent benchmarks
Arena Text
GLM-5.3-Flash:#20of 52
1,477 Elo · ahead of 61%
Inkling 2 Small: Not scored
Arena WebDev
GLM-5.3-Flash:#24of 46
1,607 Elo · ahead of 49%
Inkling 2 Small: Not scored
GPQA Diamond
GLM-5.3-Flash:#28of 41
90.2% · ahead of 30%
Inkling 2 Small: Not scored
FrontierMath Tiers 1-3 (v2)
GLM-5.3-Flash:#33of 41
55.8% · ahead of 20%
Inkling 2 Small: Not scored
Price and context
Price per 1M input tokens
GLM-5.3-Flash:#5of 58
$0.15 · cheaper than 91%
Inkling 2 Small:#11of 58
$0.30 · cheaper than 75%
Price per 1M output tokens
GLM-5.3-Flash:#3of 58
$0.50 · cheaper than 93%
Inkling 2 Small:#10of 58
$1 · cheaper than 84%
Context window
GLM-5.3-Flash:#27of 67
1M tokens · longer than 24%
Inkling 2 Small:#57of 67
262K tokens · longer than 12%
Quality against price
GLM-5.3-Flash scores 1,477 at $0.238 (blended price per 1M tokens). The line joins the current models no other beats on both price and Arena Text score; hover any point to name it.
Quality against price: the data
| Model | Provider | Blended price per 1M tokens | Arena Text (Elo) |
|---|---|---|---|
| Gemini 4 Carbon | $4 | 1,528 | |
| Gemini 4 Argon | $4 | 1,520 | |
| Claude Opus 5.5 | Anthropic | $8 | 1,509 |
| Muse Wonder | Meta | $8 | 1,507 |
| Claude Fable 5 | Anthropic | $20 | 1,504 |
| Claude Fable 5.1 | Anthropic | $20 | 1,504 |
| Gemini 3.8 Flash | $1.50 | 1,496 | |
| Muse Spark 1.3 | Meta | $2 | 1,495 |
| Claude Opus 5 | Anthropic | $10 | 1,492 |
| Kimi K3 | Moonshot Kimi | $6 | 1,488 |
| GPT-5.6 Sol | OpenAI | $8 | 1,485 |
| Gemini 3.6 Flash | $3 | 1,483 | |
| GPT-5.5 | OpenAI | $11 | 1,482 |
| Claude Opus 4.8 | Anthropic | $10 | 1,482 |
| Qwen3.8-Max | Alibaba Qwen | $3 | 1,482 |
| GPT-6.1 Sol | OpenAI | $4 | 1,481 |
| GLM-5.3 | Z.ai | $2.15 | 1,481 |
| MiMo-V2.6-Pro | Xiaomi MiMo | $0.54 | 1,480 |
| Claude Sonnet 5.5 | Anthropic | $4 | 1,477 |
| GLM-5.3-Flash | Z.ai | $0.24 | 1,477 |
| GPT-6 Astra | OpenAI | $20 | 1,476 |
| DeepSeek-V4.1-Flash | DeepSeek | $0.52 | 1,476 |
| GPT-5.4 | OpenAI | $5.63 | 1,475 |
| Gemini 3 Flash Preview | $1.13 | 1,473 | |
| Grok 4.20 | xAI | $1.56 | 1,472 |
| GPT-5.6 Terra | OpenAI | $4.50 | 1,467 |
| Grok 4.5 | xAI | $3 | 1,465 |
| DeepSeek-V4-Pro | DeepSeek | $1.98 | 1,465 |
| Claude Sonnet 5 | Anthropic | $4 | 1,462 |
| GPT-6 Sol | OpenAI | $4 | 1,458 |
| Gemini 3.5 Flash-Lite | $0.85 | 1,456 | |
| Grok 4.6 | xAI | $3 | 1,455 |
| GPT-5.6 Luna | OpenAI | $0.45 | 1,453 |
| MiMo-V2.6-Flash | Xiaomi MiMo | $0.18 | 1,448 |
| Step 5 Preview | StepFun | $1.43 | 1,448 |
| Grok 4.3 | xAI | $1.56 | 1,442 |
| GPT-6 Luna | OpenAI | $0.20 | 1,441 |
| Grok 4.7 | xAI | $3 | 1,441 |
| Mistral Large 4 | Mistral AI | $1.05 | 1,432 |
| Claude Haiku 5.5 | Anthropic | $0.20 | 1,425 |
| GPT-4.1 | OpenAI | $3.50 | 1,415 |
| Solar Pro 4 | Upstage | $0.52 | 1,388 |
| GPT-4.1 mini | OpenAI | $0.70 | 1,383 |
| GPT-4.1 nano | OpenAI | $0.18 | 1,322 |
Cost
What each costs
Example workloads at each provider's listed rates, then every other rate either one lists. The cheaper figure in each row is highlighted.
The same work, priced
For 10M input and 2M output tokens, Inkling 2 Small costs 2.0 times as much as GLM-5.3-Flash, at each provider's listed rates.
10M input and 2M output tokens
Inkling 2 Small costs 2.0× as much
- GLM-5.3-Flash
- $2.50
- Inkling 2 Small
- $5
The same, with 9M of the input tokens cached
Inkling 2 Small costs 1.8× as much
- GLM-5.3-Flash
- $1.42
- Inkling 2 Small
- $2.57
| Workload or rate | GLM-5.3-Flash | Inkling 2 Small |
|---|---|---|
| Example workloads | ||
| 10M input and 2M output tokens | $2.50 (better) | $5 |
| The same, with 9M of the input tokens cached | $1.42 (better) | $2.57 |
| Other listed rates | ||
| 1M input tokens, cached | $0.03 | $0.03 |
Your own bill will differ. These figures use list prices and example workloads. The AI Model Cost Audit reviews your real usage, shortlists models that score as well for each task for less, and estimates the savings.
Launch price $100, regular price $150
Verdict
When to choose each
GLM-5.3-Flash
Where it leads
- Lower input and output prices, $0.15 and $0.50 vs $0.30 and $1 per 1M tokens
- Larger context window 1,000,000 vs 262,144 tokens
- Accepts video input
- Open weights you can run yourself
Best for
- Low-cost coding from screenshots or screen recordings
- Document and research agents on a budget
Watch out for
- Thinking cannot be disabled
- A faster GLM-5.3-FlashX is a separate model id at $0.37 input and $1.25 output per 1M tokens
Inkling 2 Small
Where it leads
No measured lead over GLM-5.3-Flash in this data.
Best for
- Agentic coding and office, finance and legal work at a low per-token price
- Teams that want to post-train their own variant on Tinker
Watch out for
- Closed weights for now: Thinking Machines says it will publish the full weights under Apache 2.0 the week after launch, once external safety testing ends; until then Tinker is the only way to use it
- Tinker serves it with a 64K or 256K context, although the model card lists up to 1M tokens
- In its lab's own table DeepSeek-V4.1-Flash scores higher on DeepSWE v1.1 (77.0% against 72.1%) and Claude Haiku 5.5 on Vibe Code Bench (90.4% against 81.6%); Thinking Machines also says it is less tuned for chat, and no independent benchmark score is recorded here yet
From benchmark to production
Building on GLM-5.3-Flash or Inkling 2 Small?
Public scores rarely hold on your own data. We test both models on your real tasks, then build the agent or pipeline around the one that wins.
AI Model Cost AuditLaunch price $100, regular price $150What the audit includes
FAQ
GLM-5.3-Flash vs Inkling 2 Small FAQ
Which is cheaper, GLM-5.3-Flash or Inkling 2 Small?
GLM-5.3-Flash is 50% cheaper per 1M input tokens than Inkling 2 Small, $0.15 vs $0.30. For 10M input and 2M output tokens, GLM-5.3-Flash costs $2.50 and Inkling 2 Small $5 at standard rates.
Which is cheaper with prompt caching, GLM-5.3-Flash or Inkling 2 Small?
GLM-5.3-Flash charges $0.03 per 1M cached input tokens, 80% below its standard input rate, and Inkling 2 Small $0.03, 90% below. With 9M of 10M input tokens cached and 2M output tokens, GLM-5.3-Flash costs $1.42 and Inkling 2 Small $2.57.
How do GLM-5.3-Flash and Inkling 2 Small compare in Thinking Machines Lab's own results?
In the Inkling 2 Small model card (November 5, 2026), Inkling 2 Small leads on DeepSWE v1.1, 72.1% to 63.4%; Inkling 2 Small leads on Vibe Code Bench v1.1, 81.6% to 30.8%; both score 38.8% on SWE-Marathon v1.1. These are Thinking Machines Lab's figures, not independent measurements.
Which has the larger context window, GLM-5.3-Flash or Inkling 2 Small?
GLM-5.3-Flash has the larger context window, 1,000,000 vs 262,144 tokens.
How do GLM-5.3-Flash and Inkling 2 Small differ in features?
GLM-5.3-Flash takes text, image and video input; Inkling 2 Small takes text and image.
Can I run GLM-5.3-Flash or Inkling 2 Small myself?
GLM-5.3-Flash is released as open weights you can run yourself; Inkling 2 Small is available through its provider's API only.
Further reading
More on GLM-5.3-Flash and Inkling 2 Small
Related comparisons
- Claude Haiku 5.5 vs GLM-5.3-Flash
- Claude Haiku 5.5 vs Inkling 2 Small
- Claude Sonnet 5.5 vs GLM-5.3-Flash
- DeepSeek-V4.1-Flash vs Inkling 2 Small
- GLM-5.3-Flash vs GPT-6 Luna
- Gemini 3.5 Flash-Lite vs Inkling 2 Small
- GLM-5.3-Flash vs GPT-6 Sol
- Gemini 3.8 Flash vs Inkling 2 Small
- DeepSeek-V4.1-Flash vs GLM-5.3-Flash
- GLM-5.3 vs Inkling 2 Small
- GLM-5.3-Flash vs GPT-6 Astra
- GPT-6 Luna vs Inkling 2 Small
Production evaluation
Choosing between GLM-5.3-Flash and Inkling 2 Small?
Public benchmarks narrow the shortlist. We run both on your own tasks, data and cost limits, then build the routing and review controls to run the winner in production.
- A head-to-head evaluation on your real workload
- Routing between models by cost and difficulty