Updated November 7, 2026. Rank says Inkling 2 Small is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
Inkling 2 Small has the higher public point estimate, 59.06 versus 56.2. Their conditional score ranges overlap. These ranges do not establish rank confidence. 4 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
Thinking Machines Lab
59.06/100
Estimated · Public rank #56
Conditional range 44.7–73.4
Recommendations appear only when a shared evidence basis or an explicit operating constraint supports the call. Secondary and unsupported use cases stay disclosed below the initial list.
Code generation, repair, and software-engineering tasks
Inkling 2 Small
Inkling 2 Small has the higher public coding point estimate, 59.7 to 47.5, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Prompts that approach the documented context limit
Inkling 2 Small
Inkling 2 Small has the larger documented context window.
1K fresh input + 500 output tokens
Inkling 2 Small
Inkling 2 Small has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
50K fresh input + 3K output tokens
Inkling 2 Small
Inkling 2 Small has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Tool use, computer use, and multi-step task completion
Not enough matched evidence
GLM-5.1 is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
200K cached + 20K fresh input + 10K output tokens
Not enough matched evidence
The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. GLM-5.1 does not fit this workload in one request. GLM-5.1 has no published cached-input rate, so cached tokens use its listed input rate.
Choose a job from the LLM Selector's task catalog to see the category row it rests on, under the same basis rules as the table below. A directional row stays directional; choosing a job never creates a winner.
The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.
Like-for-like · BenchAlign v5.8
Inkling 2 Small has the higher coding point estimate. Conditional score ranges do not establish rank confidence.
Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
2 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
Only shared public evidence is shown. Sparse evidence stays a ruled list rather than being closed into a radar shape.
Too few matched category axes support a radar. The ruled list below shows only shared benchmark results; positions use each benchmark’s normalized display scale when available.
BrowseCompAgentic
Normalized gap 22.2HMMT Feb 2026Math
Normalized gap 13.2Each row shows the public-lane category score for both models: the BenchAlign v5.8 lane for agentic, coding, and knowledge, and the weighted public rows elsewhere, with the same rank each model page reports. A row is like-for-like only when both scores rest on Supported evidence or the same weighted set. Directional and not-comparable rows remain visible, but they never receive a winner.
| Category | GLM-5.1 | Inkling 2 Small | Basis | Reading |
|---|---|---|---|---|
| Coding | 47.5Supported · #53/157 | 59.7Supported · #20/157 | Like-for-likeBenchAlign v5.8 lane · 8 vs 6 public rows | Inkling 2 Small leads · intervals overlap |
| Agentic | 45.1Estimated · #58/131 | 59.9Supported · #26/131 | Directional onlyBenchAlign v5.8 lane · 10 vs 5 public rows | Directional only |
| Knowledge | 52.1Supported · #72/178 | 60.4Estimated · #46/178 | Directional onlyBenchAlign v5.8 lane · 4 vs 2 public rows | Directional only |
| Reasoning | 73.9Unranked · 2 rankable rows | 86.1Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
| Multimodal | Not ranked | 86.6#12/59 | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | 93.8#7/157 | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 55.9#8/12 | 85.9Unranked · 2 rankable rows | Not comparableProvisional lane · 3 vs 1 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.8) differ from the provisional-lane categories. Unranked scores sit on the provisional lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.
Three fixed token mixes turn per-token rates into comparable decisions. Each scenario states context fit and whether cached input had to fall back to the published list-input rate.
1K fresh input + 500 output tokens
Inkling 2 Small has the lower modeled cost
Costs use the listed standard API rates.
50K fresh input + 3K output tokens
Inkling 2 Small has the lower modeled cost
Costs use the listed standard API rates.
200K cached + 20K fresh input + 10K output tokens
GLM-5.1 does not fit this workload in one request. GLM-5.1 has no published cached-input rate, so cached tokens use its listed input rate.
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
Maximum documented context; output-token limits may be lower.
GLM-5.1
203K
Inkling 2 Small
GLM-5.1
Not sourced
Inkling 2 Small
Not sourced
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
GLM-5.1
Not published
Inkling 2 Small
$0.03 per 1M cached input tokens
Tinker models and pricingGLM-5.1
Not sourced
Inkling 2 Small
Not sourced
GLM-5.1
Not sourced
Inkling 2 Small
Not sourced
GLM-5.1
Not sourced
Inkling 2 Small
Not sourced
GLM-5.1
Reasoning
Inkling 2 Small
Hybrid
GLM-5.1
Open Weight
Inkling 2 Small
Proprietary
GLM-5.1
Open Weight
Inkling 2 Small
Proprietary
GLM-5.1
2026-04-07
Inkling 2 Small
2026-11-05
Inkling 2 Small has the higher public point estimate, 59.06 versus 56.2. Their conditional score ranges overlap. These ranges do not establish rank confidence. The page therefore keeps the decision tied to the specific documented workload.
Inkling 2 Small has the higher public coding point estimate, 59.7 to 47.5, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Inkling 2 Small scores higher for agentic tasks on the public lane, 59.9 to 45.1. GLM-5.1 is scored on Estimated evidence for agentic tasks, so the reading is directional rather than like-for-like. BenchLM does not name a winner for agentic tasks from a directional reading; read the shared benchmark rows directly and test the models on the same task set.
For the stated presets, chat costs $0.0036 on GLM-5.1 and $0.0008 on Inkling 2 Small; repository review costs $0.0832 and $0.018; the cache-heavy agent loop costs $0.352 and $0.022. GLM-5.1 does not fit this workload in one request. GLM-5.1 has no published cached-input rate, so cached tokens use its listed input rate.
Inkling 2 Small has the larger documented context window: 1M, compared with 203K.
Estimates at 50,000 req/day · 1000 tokens/req average.
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Terminal-Bench 2.0
Not directly comparable
BrowseComp
Inkling 2 Small leads this result
τ³-bench results
Not directly comparable
MCP Atlas
Inkling 2 Small leads this result
CyberGym
Not directly comparable
Claw-Eval
Not directly comparable
Gert Labs
Not directly comparable
ResearchClawBench
Not directly comparable
Terminal-Bench 2.1 (Vals)
Not directly comparable
HLE w/ tools
Not directly comparable
Terminal-Bench 2.1
Not directly comparable
DeepSearchQA
Not directly comparable
JobBench
Not directly comparable
SWE-bench Pro
Not directly comparable
NL2Repo
Not directly comparable
SWE-Rebench
Not directly comparable
Terminal-Bench 2.0
Not directly comparable
Vibe Code Bench
Not directly comparable
OpenHarmony Bench
Not directly comparable
LiveCodeBench (Vals)
Not directly comparable
SWE-bench (Vals)
Not directly comparable
SWE-bench Verified
Not directly comparable
SWE Multilingual
Not directly comparable
sweMarathon
Not directly comparable
Terminal-Bench 2.1
Not directly comparable
DeepSWE
Not directly comparable
PostTrainBench v1.2
Not directly comparable
LongBench v2
Not directly comparable
CharXiv
Not directly comparable
CharXiv w/o tools
Not directly comparable
Chartography (no tools)
Not directly comparable
Chartography (tools)
Not directly comparable
BabyVision
Not directly comparable
BabyVision w/ Python
Not directly comparable
ScreenSpot Pro
Not directly comparable
PerceptionBench
Not directly comparable
MathVision
Not directly comparable
GPQA-D
Inkling 2 Small leads this result
HLE
Not directly comparable
GPQA Diamond (Vals)
Not directly comparable
MMLU-Pro (Vals)
Not directly comparable
GPQA
Not directly comparable
AIME26
Not directly comparable
HMMT Nov 2025
Not directly comparable
HMMT Feb 2026
Inkling 2 Small leads this result
MMAnswerBench
Not directly comparable
FrontierMath v2 (Tiers 1-3)
Not directly comparable
Apex
Not directly comparable
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Last updated November 7, 2026
