GPT-5.6 Terra vs Qwen3.8-Flash-Next

Updated October 10, 2026. Rank says GPT-5.6 Terra is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

Decision reading

GPT-5.6 Terra has the higher public point estimate, 72.34 versus 64.23. Their conditional score ranges overlap. These ranges do not establish rank confidence. 5 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A

GPT-5.6 Terra

OpenAI

72.34/100

Supported · Public rank #13

90% interval 69.1–75.6

Model B

Qwen3.8-Flash-Next

Alibaba

64.23/100

Estimated · Public rank #37

Conditional range 49.9–78.6

Shared results: 5
GPT-5.6 Terra only: 29
Qwen3.8-Flash-Next only: 19
Like-for-like categories: 2 / 8

Supported: GPT-5.6 Terra · Estimated: Qwen3.8-Flash-Next. Conditional ranges do not establish rank confidence.How the comparison works

Which one for your work

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.

Coding work
Code generation, repair, and software-engineering tasks
GPT-5.6 Terra
GPT-5.6 Terra has the higher public coding point estimate, 63.5 to 52.4, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Confidence: limited
Long documents
Prompts that approach the documented context limit
GPT-5.6 Terra
GPT-5.6 Terra has the larger documented context window.
Confidence: documented

Show secondary and unsupported calls

Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Qwen3.8-Flash-Next is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
Confidence: limited
Chat turn cost
1K fresh input + 500 output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
Repository review cost
50K fresh input + 3K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates

Which one for a specific job

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.

Pick the job you would use them for

The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.

63.5GPT-5.6 Terra52.4Qwen3.8-Flash-Next

Like-for-like · BenchAlign v5.8

GPT-5.6 Terra 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.

Open the coding leaderboard Shortlist models for this job

Same basis rules as the category table below

Shape of the matched evidence

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.

OSWorld 2.0Agentic
Normalized gap 30.8
GPT-5.6 Terra:50.2%
Qwen3.8-Flash-Next:19.4%
GPT-5.6 Terra source Qwen3.8-Flash-Next source
GPQAKnowledge
Normalized gap 1.2
GPT-5.6 Terra:92.9%
Qwen3.8-Flash-Next:91.7%
GPT-5.6 Terra source Qwen3.8-Flash-Next source
SWE-bench ProCoding
Normalized gap 0.9
GPT-5.6 Terra:63.4%
Qwen3.8-Flash-Next:62.5%
GPT-5.6 Terra source Qwen3.8-Flash-Next source

Bars run 0–100 on each benchmark’s normalized display scale

Category results, on a stated basis

Each 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.

Coding

Like-for-like

GPT-5.6 Terra: 63.5; Supported · #12/146
Qwen3.8-Flash-Next: 52.4; Supported · #37/146
Basis: BenchAlign v5.8 lane · 9 vs 5 public rows
Reading: GPT-5.6 Terra leads · intervals overlap

Knowledge

Like-for-like

GPT-5.6 Terra: 71.5; Supported · #13/177
Qwen3.8-Flash-Next: 59.4; Supported · #47/177
Basis: BenchAlign v5.8 lane · 8 vs 4 public rows
Reading: GPT-5.6 Terra leads · intervals overlap

Agentic

Directional only

GPT-5.6 Terra: 59.3; Supported · #26/123
Qwen3.8-Flash-Next: 59.5; Estimated · #24/123
Basis: BenchAlign v5.8 lane · 9 vs 6 public rows
Reading: Directional only

Multimodal

Directional only

GPT-5.6 Terra: 84.5; #11/54
Qwen3.8-Flash-Next: 87.7; #8/54
Basis: Provisional lane · 1 vs 1 weighted rows
Reading: Directional only

Instruction following

Directional only

GPT-5.6 Terra: 87.1; #38/127
Qwen3.8-Flash-Next: 91.2; #22/127
Basis: Provisional lane · 0 vs 1 weighted rows
Reading: Directional only

Reasoning

Not comparable

GPT-5.6 Terra: 69.2; #22/28
Qwen3.8-Flash-Next: 80.9; Unranked · 2 rankable rows
Basis: Provisional lane · 2 vs 0 weighted rows
Reading: Not comparable

Multilingual

Not comparable

GPT-5.6 Terra: Not ranked
Qwen3.8-Flash-Next: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

GPT-5.6 Terra: 97.3; Unranked · 3 rankable rows
Qwen3.8-Flash-Next: Not ranked
Basis: Provisional lane · 2 vs 0 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for GPT-5.6 Terra and Qwen3.8-Flash-Next
Category	GPT-5.6 Terra	Qwen3.8-Flash-Next	Basis	Reading
Coding	63.5Supported · #12/146	52.4Supported · #37/146	Like-for-likeBenchAlign v5.8 lane · 9 vs 5 public rows	GPT-5.6 Terra leads · intervals overlap
Knowledge	71.5Supported · #13/177	59.4Supported · #47/177	Like-for-likeBenchAlign v5.8 lane · 8 vs 4 public rows	GPT-5.6 Terra leads · intervals overlap
Agentic	59.3Supported · #26/123	59.5Estimated · #24/123	Directional onlyBenchAlign v5.8 lane · 9 vs 6 public rows	Directional only
Multimodal	84.5#11/54	87.7#8/54	Directional onlyProvisional lane · 1 vs 1 weighted rows	Directional only
Instruction following	87.1#38/127	91.2#22/127	Directional onlyProvisional lane · 0 vs 1 weighted rows	Directional only
Reasoning	69.2#22/28	80.9Unranked · 2 rankable rows	Not comparableProvisional lane · 2 vs 0 weighted rows	Not comparable
Multilingual	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Math	97.3Unranked · 3 rankable rows	Not ranked	Not comparableProvisional lane · 2 vs 0 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.

Supported evidence per lane · bars run 0–100Methodology

What each workload costs

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.

Chat turn

1K fresh input + 500 output tokens

GPT-5.6 Terra: $0.008; Fits in one request
Qwen3.8-Flash-Next: Self-hosted; infrastructure cost varies; Fits in one request

Qwen3.8-Flash-Next has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

GPT-5.6 Terra: $0.136; Fits in one request
Qwen3.8-Flash-Next: Self-hosted; infrastructure cost varies; Fits in one request

Qwen3.8-Flash-Next has no comparable published API token rate.

Cache-heavy agent loop

200K cached + 20K fresh input + 10K output tokens

GPT-5.6 Terra: $0.2; Fits in one request
Qwen3.8-Flash-Next: Self-hosted; infrastructure cost varies; Fits in one request; Cached-input rate unavailable

Qwen3.8-Flash-Next has no comparable published API token rate.

Cached input falls back to the list input rate only where a cached rate is unpublished

Specification differences

Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.

SpecificationGPT-5.6 TerraQwen3.8-Flash-Next

Context window

Maximum documented context; output-token limits may be lower.

GPT-5.6 Terra

1.05M

OpenAI model catalog

Qwen3.8-Flash-Next

262K

Qwen3.8-Flash-Next model card

API model ID

GPT-5.6 Terra

gpt-5.6-terra

OpenAI model catalog

Qwen3.8-Flash-Next

Qwen/Qwen3.8-Flash-Next

Qwen3.8-Flash-Next model card

Cached-input rate

A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.

GPT-5.6 Terra

$0.2 per 1M cached input tokens

OpenAI pricing

Qwen3.8-Flash-Next

No comparable hosted API rate

Qwen3.8-Flash-Next model card

Documented inputs

GPT-5.6 Terra

text, image

OpenAI model catalog

Qwen3.8-Flash-Next

Not sourced

Documented outputs

GPT-5.6 Terra

text

OpenAI model catalog

Qwen3.8-Flash-Next

Not sourced

Provider availability

GPT-5.6 Terra

Generally Available · OpenAI Responses API

OpenAI model catalog

Qwen3.8-Flash-Next

Not sourced

Reasoning profile

GPT-5.6 Terra

Reasoning

Qwen3.8-Flash-Next

Reasoning

Weight access

GPT-5.6 Terra

Proprietary

Qwen3.8-Flash-Next

Open Weight

License

GPT-5.6 Terra

Proprietary

Qwen3.8-Flash-Next

Open Weight

Release date

GPT-5.6 Terra

2026-07-09

Qwen3.8-Flash-Next

2026-08-26

If you already use one of these models

Deployment change: The models list different providers, so authentication, endpoint behavior, limits, and feature support may change.
Quality signal: GPT-5.6 Terra has the higher public point estimate, 72.34 versus 64.23. Their conditional score ranges overlap. These ranges do not establish rank confidence.
Workload cost: A complete comparable API-rate estimate is not available for both models.
Context tradeoff: GPT-5.6 Terra has the larger documented window (1.05M).

Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, GPT-5.6 Terra or Qwen3.8-Flash-Next?

GPT-5.6 Terra has the higher public point estimate, 72.34 versus 64.23. Their conditional score ranges overlap. These ranges do not establish rank confidence. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, GPT-5.6 Terra or Qwen3.8-Flash-Next?

GPT-5.6 Terra has the higher public coding point estimate, 63.5 to 52.4, with Supported evidence for both models. The conditional ranges do not establish rank confidence.

Which is better for agentic tasks, GPT-5.6 Terra or Qwen3.8-Flash-Next?

Qwen3.8-Flash-Next scores higher for agentic tasks on the public lane, 59.5 to 59.3. Qwen3.8-Flash-Next 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.

Which costs less, GPT-5.6 Terra or Qwen3.8-Flash-Next?

Both models do not have comparable published API token rates, so this page does not name a universal price winner.

Which has the larger context window, GPT-5.6 Terra or Qwen3.8-Flash-Next?

GPT-5.6 Terra has the larger documented context window: 1.05M, compared with 262K.

Benchmark evidence

The full public result ledger is available for audit without forcing a wide desktop table onto a phone.

Browse raw public benchmark evidence53 rows

Agentic

Terminal-Bench 3.0
GPT-5.6 Terra20.8%
Source
Qwen3.8-Flash-Next—
Not directly comparable
Terminal-Bench 2.1
GPT-5.6 Terra87.4%
Source
Qwen3.8-Flash-Next—
Not directly comparable
BrowseComp
GPT-5.6 Terra87.5%
Source
Qwen3.8-Flash-Next—
Not directly comparable
OSWorld 2.0
GPT-5.6 Terra50.2%
Source
Qwen3.8-Flash-Next19.4%
Source
GPT-5.6 Terra leads this result
CyberGym
GPT-5.6 Terra81.8%
Source
Qwen3.8-Flash-Next—
Not directly comparable
ExploitGym
GPT-5.6 Terra23.2%
Source
Qwen3.8-Flash-Next—
Not directly comparable
Toolathlon
GPT-5.6 Terra53.1%
Source
Qwen3.8-Flash-Next—
Not directly comparable
Terminal-Bench 2.1 (Vals)
GPT-5.6 Terra77.5%
Source
Qwen3.8-Flash-Next—
Not directly comparable
ApprenticeBench
GPT-5.6 Terra16%
Source
Qwen3.8-Flash-Next—
Not directly comparable
CoWorkBench
GPT-5.6 Terra—
Qwen3.8-Flash-Next73.9%
Source
Not directly comparable
JobBench
GPT-5.6 Terra—
Qwen3.8-Flash-Next55.7%
Source
Not directly comparable
Agents' Last Exam
GPT-5.6 Terra—
Qwen3.8-Flash-Next51.2%
Source
Not directly comparable
Toolathlon-Verified
GPT-5.6 Terra—
Qwen3.8-Flash-Next73.5%
Source
Not directly comparable
AndroidWorld
GPT-5.6 Terra—
Qwen3.8-Flash-Next84.5%
Source
Not directly comparable

Coding

SWE-bench Pro
GPT-5.6 Terra63.4%
Source
Qwen3.8-Flash-Next62.5%
Source
GPT-5.6 Terra leads this result
Terminal-Bench 2.1
GPT-5.6 Terra87.4%
Source
Qwen3.8-Flash-Next—
Not directly comparable
DeepSWE
GPT-5.6 Terra69.6%
Source
Qwen3.8-Flash-Next58.7%
Source
GPT-5.6 Terra leads this result
FrontierCode 1.1 Extended
GPT-5.6 Terra55.8%
Source
Qwen3.8-Flash-Next—
Not directly comparable
CursorBench 3.2
GPT-5.6 Terra64.9%
Source
Qwen3.8-Flash-Next—
Not directly comparable
VulcanBench v3
GPT-5.6 Terra87.0%
Source
Qwen3.8-Flash-Next—
Not directly comparable
LiveCodeBench (Vals)
GPT-5.6 Terra85.9%
Source
Qwen3.8-Flash-Next—
Not directly comparable
SWE-bench (Vals)
GPT-5.6 Terra95.4%
Source
Qwen3.8-Flash-Next—
Not directly comparable
CursorBench 4.0
GPT-5.6 Terra41.3%
Source
Qwen3.8-Flash-Next—
Not directly comparable
SWE Multilingual
GPT-5.6 Terra—
Qwen3.8-Flash-Next81%
Source
Not directly comparable
NL2Repo
GPT-5.6 Terra—
Qwen3.8-Flash-Next48.1%
Source
Not directly comparable
LiveCodeBench v6
GPT-5.6 Terra—
Qwen3.8-Flash-Next91.9%
Source
Not directly comparable

Reasoning

ARC-AGI-2
GPT-5.6 Terra83.9%
Source
Qwen3.8-Flash-Next—
Not directly comparable
ARC-AGI-3
GPT-5.6 Terra0.8%
Source
Qwen3.8-Flash-Next—
Not directly comparable
ARC-AGI-1
GPT-5.6 Terra96.50%
Source
Qwen3.8-Flash-Next—
Not directly comparable

Multimodal

MMMU-Pro
GPT-5.6 Terra80.7%
Source
Qwen3.8-Flash-Next—
Not directly comparable
MMMU-Pro w/ Python
GPT-5.6 Terra82%
Source
Qwen3.8-Flash-Next—
Not directly comparable
Vision2Web
GPT-5.6 Terra—
Qwen3.8-Flash-Next64.0%
Source
Not directly comparable
ERQA
GPT-5.6 Terra—
Qwen3.8-Flash-Next72.3%
Source
Not directly comparable
LVBench
GPT-5.6 Terra—
Qwen3.8-Flash-Next76.6%
Source
Not directly comparable
RealWorldQA
GPT-5.6 Terra—
Qwen3.8-Flash-Next88.5%
Source
Not directly comparable
MathVision
GPT-5.6 Terra—
Qwen3.8-Flash-Next90.6%
Source
Not directly comparable
MathVision w/ Python
GPT-5.6 Terra—
Qwen3.8-Flash-Next95.7%
Source
Not directly comparable
CharXiv w/o tools
GPT-5.6 Terra—
Qwen3.8-Flash-Next84.6%
Source
Not directly comparable
CharXiv
GPT-5.6 Terra—
Qwen3.8-Flash-Next90.6%
Source
Not directly comparable

Knowledge

GPQA
GPT-5.6 Terra92.9%
Source
Qwen3.8-Flash-Next91.7%
Source
GPT-5.6 Terra leads this result
GPQA-D
GPT-5.6 Terra92.9%
Source
Qwen3.8-Flash-Next91.7%
Source
GPT-5.6 Terra leads this result
HLE-Verified
GPT-5.6 Terra51.1%
Source
Qwen3.8-Flash-Next—
Not directly comparable
LABBench2
GPT-5.6 Terra81.2%
Source
Qwen3.8-Flash-Next—
Not directly comparable
HealthBench Professional
GPT-5.6 Terra57.7%
Source
Qwen3.8-Flash-Next—
Not directly comparable
HealthBench Hard
GPT-5.6 Terra32.7%
Source
Qwen3.8-Flash-Next—
Not directly comparable
GPQA Diamond (Vals)
GPT-5.6 Terra90.9%
Source
Qwen3.8-Flash-Next—
Not directly comparable
MMLU-Pro (Vals)
GPT-5.6 Terra86.7%
Source
Qwen3.8-Flash-Next—
Not directly comparable
HLE
GPT-5.6 Terra—
Qwen3.8-Flash-Next35.9%
Source
Not directly comparable
HLE w/o tools
GPT-5.6 Terra—
Qwen3.8-Flash-Next35.9%
Source
Not directly comparable

Instruction following

IFBench
GPT-5.6 Terra—
Qwen3.8-Flash-Next81.3%
Source
Not directly comparable

Math

FrontierMath (legacy)
GPT-5.6 Terra84.9%
Source
Qwen3.8-Flash-Next—
Not directly comparable
FrontierMath v2 (Tiers 1-3)
GPT-5.6 Terra84.900%
Source
Qwen3.8-Flash-Next—
Not directly comparable
FrontierMath v2 (Tier 4)
GPT-5.6 Terra68.300%
Source
Qwen3.8-Flash-Next—
Not directly comparable

53 public results · 5 shared

Related comparisons

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Last updated October 10, 2026

GPT-5.6 Terra vs Qwen3.8-Flash-Next

Decision reading

Which one for your work

Coding work

Long documents

Agentic work

Chat turn cost

Cache-heavy agent loop cost

Repository review cost

Which one for a specific job

What is actually comparable

Shape of the matched evidence

Category results, on a stated basis

Coding

Knowledge

Agentic

Multimodal

Instruction following

Reasoning

Multilingual

Math

What each workload costs

Chat turn

Repository review

Cache-heavy agent loop

Specification differences

Context window

API model ID

Cached-input rate

Documented inputs

Documented outputs

Provider availability

Reasoning profile

Weight access

License

Release date

If you already use one of these models

Questions

Which is better, GPT-5.6 Terra or Qwen3.8-Flash-Next?

Which is better for coding, GPT-5.6 Terra or Qwen3.8-Flash-Next?

Which is better for agentic tasks, GPT-5.6 Terra or Qwen3.8-Flash-Next?

Which costs less, GPT-5.6 Terra or Qwen3.8-Flash-Next?

Which has the larger context window, GPT-5.6 Terra or Qwen3.8-Flash-Next?

Benchmark evidence

Agentic

Coding

Reasoning

Multimodal

Knowledge

Instruction following

Math

Related comparisons

Watch GPT-5.6 Terra vs Qwen3.8-Flash-Next