ZMIME
Comparison · 3 models · Updated Nov 2, 2026

Granite 4.2 8B vs Claude Mythos 5 vs Ling 3.0 Flash VL

Ling 3.0 Flash VL comes out ahead, 76 to 64 and 33 on our weighted score, though Granite 4.2 8B is 8% cheaper per token.

  1. IBM

    Granite 4.2 8B

    Released Aug 25, 2026

    64/100
    • ECI—
    • Price$0.06 / $0.15
    • Context131K
  2. Anthropic

    Claude Mythos 5

    Released Jun 9, 2026

    33/100
    • ECI—
    • Price$10.00 / $50.00
    • Context1M
  3. Our pick

    inclusionAI

    Ling 3.0 Flash VL

    Released Sep 10, 2026New

    76/100
    • ECI—
    • Price$0.06 / $0.18
    • Context262K
01 — Verdict

Ling 3.0 Flash VL is our pick

Ling 3.0 Flash VL is the better all-round choice, scoring 76/100 against Granite 4.2 8B (64) and Claude Mythos 5 (33). Granite 4.2 8B wins on price. Claude Mythos 5 wins on context window. The score weighs price 50%, inputs & features 30%, context window 20%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.

  • CapabilityNot enough dataNo independent benchmark covers every model here yet
  • Lowest priceGranite 4.2 8BGranite 4.2 8B $0.083 · Ling 3.0 Flash VL $0.09 · Claude Mythos 5 $20.00 per 1M tokens (3:1 blend)
  • Longest contextClaude Mythos 5Claude Mythos 5 1,000,000 · Ling 3.0 Flash VL 262,144 · Granite 4.2 8B 131,072 tokens
  • Widest inputsClaude Mythos 5 and Ling 3.0 Flash VLGranite 4.2 8B: Text · Claude Mythos 5: Text, Images, PDFs · Ling 3.0 Flash VL: Text, Images, Video
  • Self-hostingGranite 4.2 8B and Ling 3.0 Flash VLPublishes downloadable weights (MIT)
How the score is built
MeasureWeightGranite 4.2 8BClaude Mythos 5Ling 3.0 Flash VL
Price50%97095
Inputs & features30%357070
Context window20%256037
Overall100%64/10033/10076/100

Left out because at least one model lacks the data: capability. The remaining weights were rescaled.

02 — Side by side

Every spec in one table

Highlighted cells lead their row. Dashes mean the data is not published.

Granite 4.2 8B vs Claude Mythos 5 vs Ling 3.0 Flash VL specifications side by side
SpecificationGranite 4.2 8BIBMClaude Mythos 5AnthropicLing 3.0 Flash VLinclusionAI
Capability
Capabilities Index (ECI)———
ECI rank———
Price per million tokens
Input$0.06 (best)$10.00$0.06 (best)
Output$0.15 (best)$50.00$0.18
Cached input———
Blended (3:1)$0.083 (best)$20.00$0.09
Long-context rateSame rateSame rateSame rate
Price sourceMedian of 7 providersMedian of 2 providersMedian of 7 providers
Limits
Context window131,072 tokens1,000,000 tokens (best)262,144 tokens
Max output117,964 tokens128,000 tokens (best)32,768 tokens
Inputs and features
TextYesYesYes
ImagesNoYesYes
PDFsNoYesNo
AudioNoNoNo
VideoNoNoYes
ReasoningYesYesYes
Tool callingYesYesYes
Structured outputNoNoNo
Availability
WeightsOpenProprietaryOpenMIT
API model ID———
API providers7 (best)27 (best)
ReleasedAug 25, 2026Jun 9, 2026Sep 10, 2026
Knowledge cutoff—Jan 31, 2026—
03 — Cost

What would a month cost?

Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.

  • Granite 4.2 8B$0.90
  • Claude Mythos 5$200.00
  • Ling 3.0 Flash VL$0.96
04 — Questions

Which should you choose?

Which is better: Granite 4.2 8B, Claude Mythos 5 or Ling 3.0 Flash VL?

Ling 3.0 Flash VL is the better all-round choice, scoring 76/100 against Granite 4.2 8B (64) and Claude Mythos 5 (33). Granite 4.2 8B wins on price. Claude Mythos 5 wins on context window. The score weighs price 50%, inputs & features 30%, context window 20%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.

Which is cheaper, Granite 4.2 8B, Claude Mythos 5 or Ling 3.0 Flash VL?

Granite 4.2 8B is cheaper at $0.06 input / $0.15 output per million tokens (median across 7 API providers). Ling 3.0 Flash VL costs $0.06 input / $0.18 output per million tokens (median across 7 API providers); Claude Mythos 5 costs $10.00 input / $50.00 output per million tokens (median across 2 API providers). At a typical mix of three input tokens to one output token, that is $0.083 per million tokens for Granite 4.2 8B versus $0.09 for Ling 3.0 Flash VL (1.1× as much) and $20.00 for Claude Mythos 5 (242× as much).

Which scores higher on benchmarks?

There is no independent benchmark that covers all three models yet. Granite 4.2 8B has not been scored yet, Claude Mythos 5 has not been scored yet and Ling 3.0 Flash VL has not been scored yet.

Which is better for coding?

There are no published SWE-bench Verified results for Granite 4.2 8B, Claude Mythos 5 and Ling 3.0 Flash VL yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. All three support tool calling for agent workflows.

Which has the bigger context window?

Claude Mythos 5 has the largest context window at 1,000,000 tokens, against 262,144 for Ling 3.0 Flash VL and 131,072 for Granite 4.2 8B. Maximum output per response: Granite 4.2 8B up to 117,964, Claude Mythos 5 up to 128,000, Ling 3.0 Flash VL up to 32,768 tokens.

Which can read images, PDFs, audio or video?

Granite 4.2 8B accepts text; Claude Mythos 5 accepts text, images and PDFs; Ling 3.0 Flash VL accepts text, images and video. Claude Mythos 5 handles the widest range of inputs.

Are any of these open source?

Granite 4.2 8B and Ling 3.0 Flash VL publishes its weights (MIT) and can be self-hosted; Claude Mythos 5 is proprietary.

Which is newer?

Ling 3.0 Flash VL is the newest, released Sep 10, 2026. Granite 4.2 8B came out Aug 25, 2026; Claude Mythos 5 came out Jun 9, 2026. Knowledge cutoff: Claude Mythos 5 Jan 31, 2026.

How do you decide the winner?

Each model gets a 0–100 score on capability (50%, independent benchmark results); price (25%, blended price per million tokens (3 input : 1 output), log scale); inputs & features (15%, image, PDF, audio and video input, tool calling, structured output and reasoning); context window (10%, maximum tokens per request, log scale). Dimensions missing for any model are dropped and the remaining weights rescaled, so every model is judged on the same evidence. Specs and prices come from public model listings and the labs’ own API pages; capability scores come from independent benchmark runs. Data updated Nov 2, 2026.