Mistral Medium 2505 vs Mistral Medium 3.5
Mistral Medium 2505 (Mistral AI, 131,072-token context) versus Mistral Medium 3.5 (Mistral AI, 262,144-token context). Mistral Medium 2505 is cheaper by 73% on a blended token mix. Mistral Medium 3.5 uniquely supports vision input and native reasoning mode. Across 1 public benchmark we tracked, Mistral Medium 2505 wins 0 and Mistral Medium 3.5 wins 1. Use the live calculator below to plug your real usage shape into both, then route the winner via Agent Command Center for shadow A/B without code changes.
Bottom line — Mistral Medium 2505 vs Mistral Medium 3.5
Mistral Medium 2505 and Mistral Medium 3.5 target overlapping workloads but differ sharply on economics. Mistral Medium 2505 runs roughly 73% cheaper on a blended input-plus-output token mix, which translates to approximately $6,600 per month at mid-market volume (100K requests/day). The gap compounds at enterprise scale, making the cost axis the first filter most teams apply when deciding between these two models.
Mistral Medium 3.5 ships a 262,144-token context window, 2.0x larger than Mistral Medium 2505's 131,072 tokens. That headroom matters for long-document RAG pipelines, multi-turn agent sessions that accumulate tool-call history, and codebases where the entire repository needs to fit in a single prompt. If your average prompt stays under 131,072 tokens, the extra context on Mistral Medium 3.5 is insurance you may never use — and Mistral Medium 2505 may win on other axes.
On capability surface area, the models diverge: Mistral Medium 3.5 supports vision input where the other does not; Mistral Medium 3.5 supports native reasoning mode where the other does not. These differences are binary — either your workload needs the capability or it does not. Check whether any critical path in your agent pipeline depends on a capability only one model provides before committing to a migration.
For teams evaluating both models, the recommended path is a shadow A/B test: route production traffic through an OpenAI-compatible gateway, mirror a percentage to the candidate model, score both responses with an automated evaluator (faithfulness, tool-call correctness, latency), and compare cohort-level metrics over two weeks. Future AGI Agent Command Center supports this pattern with a single `base_url` change and built-in evaluators from the ai-evaluation SDK.
Live workload comparison
Same workload run through both models. The cheaper one is highlighted.
strategy: cost-optimized
primary:
model: mistral-medium-2505
provider: mistral
fallback:
model: mistral-medium-3-5
provider: mistral
shadow: { sample_rate: 0.05 } # mirror 5% of traffic to compare quality live| Mistral Medium 2505 | Mistral Medium 3.5 | |
|---|---|---|
| Input price | $0.400/M | $1.50/M |
| Output price | $2.00/M | $7.50/M |
| Context window | 131,072 | 262,144 |
| Max output | 8,191 | 262,144 |
| Function calling | ✓ | ✓ |
| Vision | — | ✓ |
| Audio input | — | — |
| Reasoning | — | ✓ |
| Prompt caching | — | — |
| Structured output | ✓ | ✓ |
| Pricing verified | Aug 6, 2026 | Aug 6, 2026 |
Benchmark comparison
Side-by-side public benchmark scores. Greener bar = winner.
Cost at scale: monthly spend at three usage volumes
Estimated monthly cost assuming 1,000 input + 200 output tokens per request — a realistic chat-agent shape. Adjust your own usage in the calculator at the top of this page for an exact number.
| Scale | Mistral Medium 2505 | Mistral Medium 3.5 | Delta |
|---|---|---|---|
| Startup 10K requests/day | $240 /mo | $900 /mo | $660/mo |
| Mid-market 100K requests/day | $2,400 /mo | $9,000 /mo | $6,600/mo |
| Enterprise 1M requests/day | $24,000 /mo | $90,000 /mo | $66,000/mo |
At enterprise scale (1M requests/day), a difference of even ~10% in unit price compounds into thousands of dollars per month. Cached input pricing and batch tiers can shift this further — both are surfaced on each model's own page.
When to choose which
Picked from the data above — not vendor marketing. Match the rules to your workload, not the other way around.
You're cost-sensitive at scale — Mistral Medium 2505 runs ~73% cheaper on a blended in+out token mix, compounding into thousands of dollars per month at production volume.
Your workload needs long context — Mistral Medium 3.5 fits 262,144 tokens versus the other model's 131,072, enough headroom for full books, large codebases, or 100+ page documents in one shot.
Your inputs include screenshots, diagrams, or product photos — Mistral Medium 3.5 accepts image input natively, the other doesn't.
Your tasks involve multi-step planning or math-heavy reasoning — Mistral Medium 3.5 ships a native reasoning mode that explicitly thinks before responding, the other doesn't.
On arena-elo, Mistral Medium 3.5 scores 40.0 points higher — if your workload pattern matches that benchmark's task shape, the gap is meaningful.
Capability diff — what you gain and lose on the swap
A specific list of what each model has that the other doesn't. If your workload depends on a row in Only Mistral Medium 2505, switching to Mistral Medium 3.5 means re-architecting that path (and vice versa).
- • Vision input
- • Native reasoning mode
Capabilities both share (3)
- ✓ Function calling
- ✓ Streaming
- ✓ Structured output (JSON schema)
Benchmark winners — by the numbers
For each public benchmark that has scores for both models, the higher score and the size of the gap. Benchmarks are noisy — treat anything under a 2-point delta as effectively tied.
| Benchmark | Mistral Medium 2505 | Mistral Medium 3.5 | Winner | Δ |
|---|---|---|---|---|
| arena-elo | 1387.0 | 1427.0 | Mistral Medium 3.5 | +40.0 |
Migration considerations
Concrete differences to wire through your stack before you flip traffic from one to the other.
- Context window changes up 100% when moving from Mistral Medium 2505 (131,072) to Mistral Medium 3.5 (262,144). Re-check any prompt that relies on cramming long history or documents.
- Max output tokens differ: 8,191 on Mistral Medium 2505 vs 262,144 on Mistral Medium 3.5. Long-form generation tasks may truncate differently — adjust streaming UI and chunking accordingly.
- Mistral Medium 3.5 has capabilities Mistral Medium 2505 lacks: Vision input, Native reasoning mode. Worth wiring through the agent design before commit.
How to A/B test Mistral Medium 2505 vs Mistral Medium 3.5 in production
If you're stuck between the two, run them side-by-side on real traffic. Four steps the Future AGI team uses internally:
- 1. Point your existing OpenAI SDK at
https://gateway.futureagi.com/v1. No code change beyondbase_urland a virtual key. - 2. Mark Mistral Medium 2505 primary, mirror 20% of traffic to Mistral Medium 3.5 in shadow mode. Both responses are logged; only the primary is served to users.
- 3. Score every shadow response with an evaluator — faithfulness, tool-call correctness, response latency, cost. Built-in evaluators in ai-evaluation cover the common axes.
- 4. Compare cohort-level metrics after two weeks. Switch primary when the candidate wins on what matters to your workload — and stays within your latency budget.
Full walkthrough on the Agent Command Center page.
FAQ — Mistral Medium 2505 vs Mistral Medium 3.5
Which is cheaper, Mistral Medium 2505 or Mistral Medium 3.5? ▾
Mistral Medium 2505 is cheaper by roughly 73% on a blended input + output token mix. Input prices are $0.400/M for Mistral Medium 2505 versus $1.50/M for Mistral Medium 3.5; output prices are $2.00/M versus $7.50/M. The exact savings depend on your input:output ratio — use the live calculator above to plug in your own request shape.
What is the context window of Mistral Medium 2505 versus Mistral Medium 3.5? ▾
Mistral Medium 2505 supports up to 131,072 tokens of context. Mistral Medium 3.5 supports up to 262,144 tokens. Mistral Medium 3.5 has the larger window by a factor of 2.0x, which matters for long-document RAG, multi-turn agent sessions, and tasks that need to keep an entire codebase in working memory.
Do Mistral Medium 2505 and Mistral Medium 3.5 both support tool calling? ▾
Yes — both Mistral Medium 2505 and Mistral Medium 3.5 support native function calling. Both also support structured output via JSON schema, so an agent can be ported between them with the same tool definitions.
Can Mistral Medium 2505 and Mistral Medium 3.5 process images? ▾
Mistral Medium 3.5 accepts native image input. Mistral Medium 2505 does not — you would need to route image-heavy workloads through Mistral Medium 3.5 or add a separate vision model in front of Mistral Medium 2505.
When should I choose Mistral Medium 2505 over Mistral Medium 3.5? ▾
You're cost-sensitive at scale — Mistral Medium 2505 runs ~73% cheaper on a blended in+out token mix, compounding into thousands of dollars per month at production volume.
When should I choose Mistral Medium 3.5 over Mistral Medium 2505? ▾
Your workload needs long context — Mistral Medium 3.5 fits 262,144 tokens versus the other model's 131,072, enough headroom for full books, large codebases, or 100+ page documents in one shot. Your inputs include screenshots, diagrams, or product photos — Mistral Medium 3.5 accepts image input natively, the other doesn't. Your tasks involve multi-step planning or math-heavy reasoning — Mistral Medium 3.5 ships a native reasoning mode that explicitly thinks before responding, the other doesn't. On arena-elo, Mistral Medium 3.5 scores 40.0 points higher — if your workload pattern matches that benchmark's task shape, the gap is meaningful.
How do I A/B test Mistral Medium 2505 against Mistral Medium 3.5 in production? ▾
Route both through an OpenAI-compatible gateway like Future AGI Agent Command Center with shadow mode enabled. Send 100% of traffic to your primary model, mirror 10–20% to the candidate, score every response with an evaluator (faithfulness, tool-call correctness, response time), and compare cohort-level metrics for two weeks. Switch when the candidate wins on the metrics that matter to your workload and stays within your latency budget.