GPT-5.6 Terra
Unavailable in this round
Head-to-head comparison
Compare published benchmark results from matching versions and cohorts, API pricing, and technical specifications. Any documented test setup differences remain visible.
Specifications
Release date, availability, context window, architecture, and published parameter counts at a glance. Undisclosed details remain marked as unavailable.
| Attribute | GPT-5.6 Terra | GLM-5.3-Flash |
|---|---|---|
| Provider | OpenAI | Z.ai |
| Released | Jul 9, 2026 | Aug 26, 2026 |
| Availability | Active | Open Source |
| Model type | Proprietary | Open Weights |
| Parameters | Not published | 320B, 18B active |
| Architecture | Not published | Mixture of Experts |
| Context window | 1,050,000 tokens | 1,000,000 tokens |
| Knowledge cutoff | February 16, 2026 | Not published |
| Technical sources | Model, Context, Knowledge cutoff | Model, Context |
Pricing
The table compares official standard API rates per 1M tokens, including published cache pricing. Batch, fast, priority, regional surcharges, tool calls, and cloud platform pricing are excluded.
| Price type | GPT-5.6 Terra | GLM-5.3-Flash |
|---|---|---|
| API input | $2 ≤272K / $4 >272K | $0.15 |
| API output | $12 ≤272K / $18 >272K | $0.5 |
| Cache read | $0.2 ≤272K / $0.4 >272K | $0.03 |
| Cache write (5 min.) | $2.5 ≤272K / $5 >272K | Not listed |
| Cache write (1 hr.) | Not listed | Not listed |
| Price verified | 09/22/2026 Source | 09/22/2026 Source |
Key to the marks:better valuebetter, but not conclusivetie
Direct developer pricing and provider endpoint tariffs routed through OpenRouter are listed separately. Prices are in USD per 1M tokens. Context-dependent direct tiers plus regional, Flex, Priority, and other OpenRouter tariffs remain individually identifiable.
| Model | Provider | Purchase route and tariff | Input per 1M tokens | Output per 1M tokens | Source |
|---|---|---|---|---|---|
| GPT-5.6 Terra | OpenAI | Direct from developerUp to 272,000 context tokens | $2 | $12 | Source09/22/2026 |
| OpenAI | Direct from developerAbove 272,000 context tokens | $4 | $18 | Source09/22/2026 | |
| Amazon Bedrock | Via OpenRouteramazon-bedrock/us-east-1 | $2.2 | $13.2 | Source10/15/2026 | |
| Azure | Via OpenRouterazure | $2 | $12 | Source10/15/2026 | |
| Azure | Via OpenRouterazure/eu | $2.2 | $13.2 | Source10/15/2026 | |
| Azure | Via OpenRouterazure/us | $2.2 | $13.2 | Source10/15/2026 | |
| OpenAI | Via OpenRouteropenai | $2 | $12 | Source10/15/2026 | |
| OpenAI | Via OpenRouteropenai/fast | $4 | $24 | Source10/15/2026 | |
| OpenAI | Via OpenRouteropenai/flex | Lowest listed price: $1 | Lowest listed price: $6 | Source10/15/2026 | |
| GLM-5.3-Flash | Z.ai | Direct from developerStandard API | $0.15 | $0.5 | Source09/22/2026 |
| AtlasCloud | Via OpenRouteratlas-cloud/fp8 | $0.15 | $0.5 | Source10/15/2026 | |
| CoreWeave | Via OpenRoutercoreweave/nvfp4 | $0.15 | $0.5 | Source10/15/2026 | |
| Crusoe | Via OpenRoutercrusoe/fp4 | $0.15 | $0.5 | Source10/15/2026 | |
| Decart | Via OpenRouterdecart/fp4 | $0.093 | $0.31 | Source10/15/2026 | |
| DeepInfra | Via OpenRouterdeepinfra/fp4 | $0.075 | $0.25 | Source10/15/2026 | |
| DigitalOcean | Via OpenRouterdigitalocean | $0.1312 | $0.4375 | Source10/15/2026 | |
| Fireworks | Via OpenRouterfireworks | $0.15 | $0.5 | Source10/15/2026 | |
| Fireworks | Via OpenRouterfireworks/us | $0.225 | $0.75 | Source10/15/2026 | |
| Friendli | Via OpenRouterfriendli | $0.15 | $0.5 | Source10/15/2026 | |
| Inceptron | Via OpenRouterinceptron/fp8 | $0.108 | $0.55 | Source10/15/2026 | |
| InferenceNet | Via OpenRouterinference-net/fp4 | $0.083 | $0.15 | Source10/15/2026 | |
| Makora | Via OpenRoutermakora | $0.14 | $0.47 | Source10/15/2026 | |
| Modal | Via OpenRoutermodal/nvfp4 | $0.15 | $0.5 | Source10/15/2026 | |
| Morph | Via OpenRoutermorph/fp8 | $0.074 | $1 | Source10/15/2026 | |
| Near AI | Via OpenRouternear-ai/fp8 | $0.08 | $0.5 | Source10/15/2026 | |
| NextBit | Via OpenRouternextbit/fp4 | $0.19 | $0.6 | Source10/15/2026 | |
| Novita | Via OpenRouternovita/fp8 | $0.084 | $0.28 | Source10/15/2026 | |
| OpenInference | Via OpenRouteropen-inference/fp4 | Lowest listed price: $0.036 | $0.9 | Source10/15/2026 | |
| Parasail | Via OpenRouterparasail/fast | $0.1875 | $0.625 | Source10/15/2026 | |
| Parasail | Via OpenRouterparasail/fp4 | $0.15 | $0.5 | Source10/15/2026 | |
| Reka | Via OpenRouterreka | $0.06 | $1.6 | Source10/15/2026 | |
| Relace | Via OpenRouterrelace | $0.04 | $0.5 | Source10/15/2026 | |
| Sail Research | Via OpenRoutersail-research/fp4 | $0.045 | $0.6 | Source10/15/2026 | |
| Sail Research | Via OpenRoutersail-research/us | $0.045 | $0.6 | Source10/15/2026 | |
| SiliconFlow | Via OpenRoutersiliconflow/fp8 | $0.15 | $0.5 | Source10/15/2026 | |
| StreamLake | Via OpenRouterstreamlake/fp8 | $0.0435 | Lowest listed price: $0.145 | Source10/15/2026 | |
| Together | Via OpenRoutertogether | $0.15 | $0.5 | Source10/15/2026 | |
| Wafer | Via OpenRouterwafer | $0.12 | $0.5 | Source10/15/2026 | |
| Z.AI | Via OpenRouterz-ai/fp8 | $0.15 | $0.5 | Source10/15/2026 |
Performance
Only published results with the same benchmark version, task, metric, and comparison cohort are paired. Different reasoning levels, agents, harnesses, or output limits appear directly in the table.
Each axis averages directly matched benchmark families. A value of 100 means the stronger value within this pair, not a universal quality score.
25 matched rows from 24 benchmark families were considered. The radar shows 8 of 12 comparable categories.
Key to the marks:better valuebetter, but not conclusivetie
| Benchmark and task | GPT-5.6 Terra | GLM-5.3-Flash | Source |
|---|---|---|---|
Code MigrationOverallTest detailsNo documented difference in the test setupVersion: 1Metric: accuracyScoring: Higher is better | Winner47.804 % | 20.515 % | Vals AISource detailsData as of: 09/27/2026 |
CyberBench v1.1OverallTest detailsNo documented difference in the test setupVersion: 1.1Metric: accuracyScoring: Higher is better | Winner72.084 % | 67.857 % | Vals AISource detailsData as of: 09/27/2026 |
EMBOverallTest detailsNo documented difference in the test setupVersion: 1Metric: accuracyScoring: Higher is better | Winner66.205 % | 55.932 % | Vals AISource detailsData as of: 09/27/2026 |
Finance Agent (v2)OverallTest detailsNo documented difference in the test setupVersion: 2Metric: accuracyScoring: Higher is better | 54.436 % | Winner57.85 % | Vals AISource detailsData as of: 09/27/2026 |
GPQA DiamondOverallTest detailsNo documented difference in the test setupVersion: 1Metric: accuracyScoring: Higher is better | Winner90.909 % | 86.364 % | Vals AISource detailsData as of: 09/01/2026 |
Harvey's Legal Agent BenchmarkOverall · Task fully resolvedTest detailsNo documented difference in the test setupVersion: 1Metric: task resolution rateScoring: Higher is better | 0.833 % | Winner6.667 % | Vals AISource detailsData as of: 09/27/2026 |
IOIOverallTest detailsNo documented difference in the test setupVersion: 2Metric: accuracyScoring: Higher is better | Winner87.611 % | 52.5 % | Vals AISource detailsData as of: 09/27/2026 |
Legal Research BenchOverall · All-passTest detailsNo documented difference in the test setupVersion: 1Metric: all-pass rateScoring: Higher is better | 41.346 % | Winner45.192 % | Vals AISource detailsData as of: 09/27/2026 |
LegalBenchOverallTest detailsNo documented difference in the test setupVersion: 1Metric: accuracyScoring: Higher is better | Winner85.105 % | 83.928 % | Vals AISource detailsData as of: 09/27/2026 |
LiveCodeBench v6OverallTest detailsNo documented difference in the test setupVersion: 1Metric: accuracyScoring: Higher is better | Winner85.93 % | 80.51 % | Vals AISource detailsData as of: 09/01/2026 |
MedScribeOverallTest detailsNo documented difference in the test setupVersion: 1Metric: accuracyScoring: Higher is better | 82.869 % | Winner88.936 % | Vals AISource detailsData as of: 09/26/2026 |
MMLU-ProOverallTest detailsNo documented difference in the test setupVersion: 1Metric: accuracyScoring: Higher is better | Winner86.659 % | 86.059 % | Vals AISource detailsData as of: 09/01/2026 |
MMMU-ProOverallTest detailsNo documented difference in the test setupVersion: 1Metric: accuracyScoring: Higher is better | Winner86.474 % | 86.012 % | Vals AISource detailsData as of: 09/01/2026 |
ProgramBenchOverallTest detailsNo documented difference in the test setupVersion: 1Metric: accuracyScoring: Higher is better | Winner0.5 % | 0 % | Vals AISource detailsData as of: 09/27/2026 |
ProofBenchOverallTest detailsNo documented difference in the test setupVersion: 1.1Metric: accuracyScoring: Higher is better | Winner74 % | 21 % | Vals AISource detailsData as of: 09/28/2026 |
SkillsBenchOverallTest detailsNo documented difference in the test setupVersion: 1Metric: accuracyScoring: Higher is betterSettings: Harness: OpenHands | Winner58.895 % | 40.159 % | Vals AISource detailsData as of: 09/27/2026 |
SWE-bench VerifiedOverallTest detailsNo documented difference in the test setupVersion: 1Metric: accuracyScoring: Higher is better | Winner95.4 % | 92 % | Vals AISource detailsData as of: 09/01/2026 |
Tax Agent BenchOverallTest detailsNo documented difference in the test setupVersion: 1Metric: accuracyScoring: Higher is better | Winner65.199 % | 61.956 % | Vals AISource detailsData as of: 09/27/2026 |
TaxEval v2OverallTest detailsNo documented difference in the test setupVersion: 2Metric: accuracyScoring: Higher is better | Winner76.166 % | 75.593 % | Vals AISource detailsData as of: 09/01/2026 |
Terminal-Bench 2.1OverallTest detailsNo documented difference in the test setupVersion: 2.1Metric: accuracyScoring: Higher is better | Winner77.528 % | 62.921 % | Vals AISource detailsData as of: 09/27/2026 |
Terminal-Bench 4.0OverallTest detailsNo documented difference in the test setupVersion: 4.0Metric: accuracyScoring: Higher is better | Winner26.263 % | 19.697 % | Vals AISource detailsData as of: 09/27/2026 |
Terminal-Bench ScienceOverallTest detailsNo documented difference in the test setupVersion: 0.1Metric: accuracyScoring: Higher is better | Winner10 % | 0 % | Vals AISource detailsData as of: 09/27/2026 |
Vals IndexOverallTest detailsNo documented difference in the test setupVersion: 2Metric: weighted index scoreScoring: Higher is better | Winner59.588 % | 47.216 % | Vals AISource detailsData as of: 09/27/2026 |
Vibe Code Bench 1-100OverallTest detailsNo documented difference in the test setupVersion: 1.0Metric: accuracyScoring: Higher is betterSettings: Harness: OpenHands | 14.822 % | Winner16.033 % | Vals AISource detailsData as of: 09/22/2026 |
Vibe Code Bench v1.1OverallTest detailsNo documented difference in the test setupVersion: 1.1Metric: accuracyScoring: Higher is betterSettings: Harness: OpenHands | Winner74.594 % | 30.759 % | Vals AISource detailsData as of: 09/27/2026 |
Key to the marks:better valuebetter, but not conclusivetie | |||
Our model tests
One SVG prompt and fixed output settings. Captures are shown only with 0 reported reasoning tokens or documented lack of reasoning controls. Identical test conditions do not make the models' internal compute identical.
Shared test round: 4ae5d8eeb9a9
Unavailable in this round
Unavailable in this round
A community classic for free-form SVG drawing.
GPT-5.6 Terra

Reasoning (requested): high
Visually reviewed. This individual test does not establish overall model quality.
GLM-5.3-Flash

Reasoning (requested): high
Visually reviewed. This individual test does not establish overall model quality.
Generate an SVG of a pelican riding a bicycle
Token limit including reasoning: 8,192 tokens
Task origin (Simon Willison)How to read this comparison
Your workload, budget, and required context length matter most. A coding benchmark says little about visual understanding or agent performance.
The standard price table uses direct developer rates. The provider comparison labels OpenRouter endpoints, regions, and special tariffs separately.
Every result links to its measurement source and retrieval date. Labels distinguish vendor reports, official benchmark leaderboards, and independent evaluations. Disputed or archived results are excluded.
More matchups
Compare GPT-5.6 Terra and GLM-5.3-Flash with other leading models using the same data and benchmark logic.