Inworld TTS-1.5 Max vs Qwen3.8 Max

Decision reading

The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. Use the documented cost, context, and runtime rows instead.

0 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A

Inworld TTS-1.5 Max

Inworld

—

Evidence status unavailable

90% interval unavailable

Model B

Qwen3.8 Max

Alibaba

73.17/100

Supported · Public rank #9

90% interval 68.9–77.4

Updated September 18, 2026. We do not rank this pair: at least one has no public score. Public scores include evidence status and uncertainty.

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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.

No workload recommendation clears the current evidence threshold.

Use the matched evidence, workload costs, and sourced specifications below instead of treating a point score as a universal answer.

Show secondary and unsupported calls

Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Inworld TTS-1.5 Max is not ranked on the public lane for coding, so no winner is named for coding.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Inworld TTS-1.5 Max is not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limited
Long documents
Prompts that approach the documented context limit
Not enough matched evidence
A complete context comparison is not sourced.
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.

—Inworld TTS-1.5 Max60.7Qwen3.8 Max

Not comparable · BenchAlign

The coding row is not comparable on the public lane: at least one model is not measured or not ranked there.

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

What is actually comparable

Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.

60 Qwen3.8 Max only

Shared results: 0
Inworld TTS-1.5 Max only: 0
Qwen3.8 Max only: 60
Like-for-like categories: 0 / 8

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign 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.

Agentic

Not comparable

Inworld TTS-1.5 Max: Not ranked
Qwen3.8 Max: 67.3; Supported · #9/154
Basis: BenchAlign lane · 0 vs 15 public rows
Reading: Not comparable

Coding

Not comparable

Inworld TTS-1.5 Max: Not ranked
Qwen3.8 Max: 60.7; Supported · #20/154
Basis: BenchAlign lane · 0 vs 12 public rows
Reading: Not comparable

Reasoning

Not comparable

Inworld TTS-1.5 Max: Not ranked
Qwen3.8 Max: 86.6; Unranked · 2 rankable rows
Basis: Provisional lane · 0 vs 2 weighted rows
Reading: Not comparable

Knowledge

Not comparable

Inworld TTS-1.5 Max: Not ranked
Qwen3.8 Max: 69.1; Supported · #17/184
Basis: BenchAlign lane · 0 vs 6 public rows
Reading: Not comparable

Math

Not comparable

Inworld TTS-1.5 Max: Not ranked
Qwen3.8 Max: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Multilingual

Not comparable

Inworld TTS-1.5 Max: Not ranked
Qwen3.8 Max: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Multimodal

Not comparable

Inworld TTS-1.5 Max: Not ranked
Qwen3.8 Max: 87.4; #5/48
Basis: Provisional lane · 0 vs 2 weighted rows
Reading: Not comparable

Instruction following

Not comparable

Inworld TTS-1.5 Max: Not ranked
Qwen3.8 Max: 90.5; #16/124
Basis: Provisional lane · 0 vs 1 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for Inworld TTS-1.5 Max and Qwen3.8 Max
Category	Inworld TTS-1.5 Max	Qwen3.8 Max	Basis	Reading
Agentic	Not ranked	67.3Supported · #9/154	Not comparableBenchAlign lane · 0 vs 15 public rows	Not comparable
Coding	Not ranked	60.7Supported · #20/154	Not comparableBenchAlign lane · 0 vs 12 public rows	Not comparable
Reasoning	Not ranked	86.6Unranked · 2 rankable rows	Not comparableProvisional lane · 0 vs 2 weighted rows	Not comparable
Knowledge	Not ranked	69.1Supported · #17/184	Not comparableBenchAlign lane · 0 vs 6 public rows	Not comparable
Math	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Multilingual	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Multimodal	Not ranked	87.4#5/48	Not comparableProvisional lane · 0 vs 2 weighted rows	Not comparable
Instruction following	Not ranked	90.5#16/124	Not comparableProvisional lane · 0 vs 1 weighted rows	Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign) differ from the provisional-lane categories. Unranked scores sit on the lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.

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.

A shared-evidence shape is not available.

BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.

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

Inworld TTS-1.5 Max: API rate not published; Fit state unavailable
Qwen3.8 Max: API rate not published; Fits in one request

Inworld TTS-1.5 Max has no comparable published API token rate. Qwen3.8 Max has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Inworld TTS-1.5 Max: API rate not published; Fit state unavailable
Qwen3.8 Max: API rate not published; Fits in one request

Inworld TTS-1.5 Max has no comparable published API token rate. Qwen3.8 Max has no comparable published API token rate.

Cache-heavy agent loop

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

Inworld TTS-1.5 Max: API rate not published; Fit state unavailable; Cached-input rate unavailable
Qwen3.8 Max: API rate not published; Fits in one request; Cached-input rate unavailable

Inworld TTS-1.5 Max has no comparable published API token rate. Qwen3.8 Max has no comparable published API token rate.

Specification differences

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

SpecificationInworld TTS-1.5 MaxQwen3.8 Max

Context window

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

Inworld TTS-1.5 Max

N/A

Qwen3.8 Max

Alibaba Cloud Model Studio pricing

API model ID

Inworld TTS-1.5 Max

Not sourced

Qwen3.8 Max

qwen3.8-max

Alibaba Cloud Model Studio pricing

Cached-input rate

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

Inworld TTS-1.5 Max

No comparable hosted API rate

Qwen3.8 Max

No comparable hosted API rate

Alibaba Cloud Model Studio pricing

Documented inputs

Inworld TTS-1.5 Max

Not sourced

Qwen3.8 Max

Not sourced

Documented outputs

Inworld TTS-1.5 Max

Not sourced

Qwen3.8 Max

Not sourced

Provider availability

Inworld TTS-1.5 Max

Not sourced

Qwen3.8 Max

Not sourced

Reasoning profile

Inworld TTS-1.5 Max

Non-Reasoning

Qwen3.8 Max

Reasoning

Weight access

Inworld TTS-1.5 Max

Proprietary

Qwen3.8 Max

Open Weight

License

Inworld TTS-1.5 Max

Proprietary

Qwen3.8 Max

Open Weight

Release date

Inworld TTS-1.5 Max

2026-01-21

Qwen3.8 Max

2026-08-03

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: The public evidence has no benchmark result shared by both models, so it does not support a quality verdict.
Workload cost: A complete comparable API-rate estimate is not available for both models.
Context tradeoff: A complete documented context comparison is not available.

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

Benchmark evidence

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

Browse raw public benchmark evidence60 rows

Agentic

Terminal-Bench 2.1
Inworld TTS-1.5 Max—
Qwen3.8 Max86.6%
Source
Not directly comparable
CoWorkBench
Inworld TTS-1.5 Max—
Qwen3.8 Max74.8%
Source
Not directly comparable
JobBench
Inworld TTS-1.5 Max—
Qwen3.8 Max53.4%
Source
Not directly comparable
skillsBench
Inworld TTS-1.5 Max—
Qwen3.8 Max70.2%
Source
Not directly comparable
Agents' Last Exam
Inworld TTS-1.5 Max—
Qwen3.8 Max52.4%
Source
Not directly comparable
AutomationBench
Inworld TTS-1.5 Max—
Qwen3.8 Max27.3%
Source
Not directly comparable
Toolathlon-Verified
Inworld TTS-1.5 Max—
Qwen3.8 Max72.5%
Source
Not directly comparable
WideResearch
Inworld TTS-1.5 Max—
Qwen3.8 Max81.9%
Source
Not directly comparable
HLE w/ tools
Inworld TTS-1.5 Max—
Qwen3.8 Max56.2%
Source
Not directly comparable
OSWorld-Verified
Inworld TTS-1.5 Max—
Qwen3.8 Max86.1%
Source
Not directly comparable
OSWorld 2.0
Inworld TTS-1.5 Max—
Qwen3.8 Max19.4%
Source
Not directly comparable
WebArena-Verified
Inworld TTS-1.5 Max—
Qwen3.8 Max66.8%
Source
Not directly comparable
AndroidWorld
Inworld TTS-1.5 Max—
Qwen3.8 Max85.3%
Source
Not directly comparable
MobileWorld
Inworld TTS-1.5 Max—
Qwen3.8 Max77.8%
Source
Not directly comparable
Terminal-Bench 2.1 (Vals)
Inworld TTS-1.5 Max—
Qwen3.8 Max67.4%
Source
Not directly comparable

Coding

Terminal-Bench 2.1
Inworld TTS-1.5 Max—
Qwen3.8 Max86.6%
Source
Not directly comparable
SWE-bench Pro
Inworld TTS-1.5 Max—
Qwen3.8 Max67.7%
Source
Not directly comparable
DeepSWE
Inworld TTS-1.5 Max—
Qwen3.8 Max56.6%
Source
Not directly comparable
NL2Repo
Inworld TTS-1.5 Max—
Qwen3.8 Max55.9%
Source
Not directly comparable
FrontierSWE
Inworld TTS-1.5 Max—
Qwen3.8 Max73.5%
Source
Not directly comparable
MLS-Bench Lite
Inworld TTS-1.5 Max—
Qwen3.8 Max41.0%
Source
Not directly comparable
PaperBench
Inworld TTS-1.5 Max—
Qwen3.8 Max93.0%
Source
Not directly comparable
VulcanBench v3
Inworld TTS-1.5 Max—
Qwen3.8 Max81.2%
Source
Not directly comparable
OpenHarmony Bench
Inworld TTS-1.5 Max—
Qwen3.8 Max60.8%
Source
Not directly comparable
FrontierSWE v2
Inworld TTS-1.5 Max—
Qwen3.8 Max15.8%
Source
Not directly comparable
LiveCodeBench (Vals)
Inworld TTS-1.5 Max—
Qwen3.8 Max87.9%
Source
Not directly comparable
SWE-bench (Vals)
Inworld TTS-1.5 Max—
Qwen3.8 Max85.6%
Source
Not directly comparable

Reasoning

MRCRv2
Inworld TTS-1.5 Max—
Qwen3.8 Max92.9%
Source
Not directly comparable
LongBench v2
Inworld TTS-1.5 Max—
Qwen3.8 Max66.3%
Source
Not directly comparable

Knowledge

GPQA
Inworld TTS-1.5 Max—
Qwen3.8 Max92.6%
Source
Not directly comparable
GPQA-D
Inworld TTS-1.5 Max—
Qwen3.8 Max92.6%
Source
Not directly comparable
HLE
Inworld TTS-1.5 Max—
Qwen3.8 Max43.6%
Source
Not directly comparable
HLE w/o tools
Inworld TTS-1.5 Max—
Qwen3.8 Max43.6%
Source
Not directly comparable
GPQA Diamond (Vals)
Inworld TTS-1.5 Max—
Qwen3.8 Max93.7%
Source
Not directly comparable
MMLU-Pro (Vals)
Inworld TTS-1.5 Max—
Qwen3.8 Max88.6%
Source
Not directly comparable

Multimodal

MMMU-Pro
Inworld TTS-1.5 Max—
Qwen3.8 Max82.3%
Source
Not directly comparable
MathVision
Inworld TTS-1.5 Max—
Qwen3.8 Max95.2%
Source
Not directly comparable
MathVision w/ Python
Inworld TTS-1.5 Max—
Qwen3.8 Max97.7%
Source
Not directly comparable
BabyVision
Inworld TTS-1.5 Max—
Qwen3.8 Max82.0%
Source
Not directly comparable
BabyVision w/ Python
Inworld TTS-1.5 Max—
Qwen3.8 Max91.3%
Source
Not directly comparable
ZeroBench
Inworld TTS-1.5 Max—
Qwen3.8 Max24.0%
Source
Not directly comparable
ZeroBench w/ Python
Inworld TTS-1.5 Max—
Qwen3.8 Max49.0%
Source
Not directly comparable
MedXpertQA (MM)
Inworld TTS-1.5 Max—
Qwen3.8 Max80.4%
Source
Not directly comparable
ScreenSpot Pro
Inworld TTS-1.5 Max—
Qwen3.8 Max84.5%
Source
Not directly comparable
Vision2Web
Inworld TTS-1.5 Max—
Qwen3.8 Max69.0%
Source
Not directly comparable
CharXiv w/o tools
Inworld TTS-1.5 Max—
Qwen3.8 Max88.4%
Source
Not directly comparable
CharXiv
Inworld TTS-1.5 Max—
Qwen3.8 Max93.5%
Source
Not directly comparable
OmniDocBench 1.5
Inworld TTS-1.5 Max—
Qwen3.8 Max92.1%
Source
Not directly comparable
OCRBench V2
Inworld TTS-1.5 Max—
Qwen3.8 Max74.2%
Source
Not directly comparable
CC-OCR
Inworld TTS-1.5 Max—
Qwen3.8 Max79.6%
Source
Not directly comparable
RealWorldQA
Inworld TTS-1.5 Max—
Qwen3.8 Max88.0%
Source
Not directly comparable
ERQA
Inworld TTS-1.5 Max—
Qwen3.8 Max77.8%
Source
Not directly comparable
SimpleVQA
Inworld TTS-1.5 Max—
Qwen3.8 Max75.0%
Source
Not directly comparable
PerceptionBench
Inworld TTS-1.5 Max—
Qwen3.8 Max63.5%
Source
Not directly comparable
Video-MME (with subtitle)
Inworld TTS-1.5 Max—
Qwen3.8 Max90.4%
Source
Not directly comparable
VideoMMMU
Inworld TTS-1.5 Max—
Qwen3.8 Max88.7%
Source
Not directly comparable
MMVU
Inworld TTS-1.5 Max—
Qwen3.8 Max82.4%
Source
Not directly comparable
MLVU (M-Avg)
Inworld TTS-1.5 Max—
Qwen3.8 Max90.8%
Source
Not directly comparable
LVBench
Inworld TTS-1.5 Max—
Qwen3.8 Max81.8%
Source
Not directly comparable

Instruction following

IFBench
Inworld TTS-1.5 Max—
Qwen3.8 Max82.8%
Source
Not directly comparable

Questions

Which is better, Inworld TTS-1.5 Max or Qwen3.8 Max?

The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, Inworld TTS-1.5 Max or Qwen3.8 Max?

Inworld TTS-1.5 Max is not ranked on the public lane for coding, so no winner is named for coding.

Which is better for agentic tasks, Inworld TTS-1.5 Max or Qwen3.8 Max?

Inworld TTS-1.5 Max is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, Inworld TTS-1.5 Max or Qwen3.8 Max?

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, Inworld TTS-1.5 Max or Qwen3.8 Max?

A complete documented context-window comparison is not available.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated September 18, 2026

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Inworld TTS-1.5 Max vs Qwen3.8 Max

Which one for your work

Coding work

Agentic work

Long documents

Chat turn cost

Cache-heavy agent loop cost

Repository review cost

Which one for a specific job

What is actually comparable

Category results, on a stated basis

Agentic

Coding

Reasoning

Knowledge

Math

Multilingual

Multimodal

Instruction following

Shape of the matched evidence

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

Benchmark evidence

Agentic

Coding

Reasoning

Knowledge

Multimodal

Instruction following

Questions

Which is better, Inworld TTS-1.5 Max or Qwen3.8 Max?

Which is better for coding, Inworld TTS-1.5 Max or Qwen3.8 Max?

Which is better for agentic tasks, Inworld TTS-1.5 Max or Qwen3.8 Max?

Which costs less, Inworld TTS-1.5 Max or Qwen3.8 Max?

Which has the larger context window, Inworld TTS-1.5 Max or Qwen3.8 Max?

Related comparisons

Watch Inworld TTS-1.5 Max vs Qwen3.8 Max